Systems and methods for delta-sigma digitization

ABSTRACT

A baseband processing unit includes a baseband processor configured to receive a plurality of component carriers of a radio access technology wireless service, and a delta-sigma digitization interface configured to digitize at least one carrier signal of the plurality of component carriers into a digitized bit stream, for transport over a transport medium, by (i) oversampling the at least one carrier signal, (ii) quantizing the oversampled carrier signal into the digitized bit stream using two or fewer quantization bits.

CROSS REFERENCE TO RELATED APPLICATIONS

This application is a continuation in part of U.S. application Ser. No.16/288,057, filed Feb. 27, 2019. U.S. application Ser. No. 16/288,057 isa continuation in part of U.S. application Ser. No. 16/283,520, filedFeb. 22, 2019. U.S. application Ser. No. 16/283,520 is a continuation inpart of U.S. application Ser. No. 16/191,315, filed Nov. 14, 2018. U.S.application Ser. No. 16/288,057 further claims the benefit of andpriority to U.S. Provisional Patent Application Ser. No. 62/635,629,filed Feb. 27, 2018. U.S. application Ser. No. 16/283,520 further claimsthe benefit of and priority to U.S. Provisional Patent Application Ser.No. 62/633,956, filed Feb. 22, 2018. U.S. application Ser. No.16/191,315 further claims the benefit of and priority to U.S.Provisional Patent Application Ser. No. 62/586,041, filed Nov. 14, 2017.The present application additionally claims the benefit of and priorityto U.S. Provisional Patent Application Ser. No. 62/660,322, filed Apr.20, 2018. The disclosures of all of these applications are incorporatedherein by reference in their entireties.

BACKGROUND

The field of the disclosure relates generally to communication networks,and more particularly, to digitization techniques in accesscommunication networks.

Emerging video-intensive and bandwidth-consuming services, such asvirtual reality (VR), augmented reality (AR), and immersiveapplications, are driving the growth of wireless data traffic in asignificant manner. This rapid growth has made the network segment ofmobile fronthaul (MFH) networks a new bottleneck of user experience.Various technologies have been proposed and investigated to increase thespectral efficiency of MFH networks and enhance the quality of services(QoS) for end users, such as analog MFH based on radio-over-fiber (RoF)technology and digital MFH based on common public radio interface(CPRI), etc. These conventional proposals, however, have been unable tokeep up with the increasing pace of growth of wireless traffic.

In a new paradigm of 5G new radio (5G-NR), heterogeneous MFH networksare proposed to aggregate wireless services from multiple radio accesstechnologies (multi-RATs), and then deliver the aggregated services in ashared ubiquitous access network, as described further below withrespect to FIG. 1.

FIG. 1 is a schematic illustration of a conventional access networkarchitecture 100. Architecture 100 includes a core network 102, abaseband processing unit (BBU) pool 104, and one or more remote radioheads (RRHs) 106 (e.g., RRHs 106(1), and mobile users 106(2) andwireless users 106(3), which connect with a respective RRH 106(1)).Architecture 100 is, in this example, a cloud-radio access network(C-RAN) that includes a plurality of centralized BBUs 108 in BBU pool104 to enable inter-cell processing. Core network 102 includes one ormore service gateways (S-GWs) 110, or mobile management entities (MMEs),in operable communication with BBU pool 104 over a mobile backhaul (MBH)network 112. That is, MBH network 112 constitutes the network segmentfrom S-GW/MME 110 to BBUs 108 or BBU pool 104. In a similar fashion, amobile fronthaul (MFH) 114 is defined as the network segment from BBUs108/BBU pool 104 to RRHs 106.

In operation of architecture 100, MBH 112 transmits digital bits 116 ofnet information, whereas MFH 114 transmits wireless services 118 ineither an analog waveform 120 based on RoF technology, or in a digitalwaveform 122 using a digitization interface, such as CPRI. In theembodiment depicted in FIG. 1, architecture 100 represents aheterogeneous MFH network, for aggregating and delivering a plurality ofservices 124 from different radio access technologies (RATs), includingWi-Fi, 4G long term evolution (4G-LTE), and 5G-NR, to RRHs 106 by way ofa shared fiber link 126. Service aggregation of the same RAT (e.g.,Wi-Fi channel boning, LTE carrier aggregation (CA), etc.) is referred toas intra-RAT aggregation, whereas heterogeneous aggregation of servicesfrom different RATs is referred to as inter-RAT aggregation. Aheterogeneous MFH network offers traffic offloading among different RATsand enhances the seamless coverage and provides a ubiquitous accessexperience to end users.

Accordingly, the conventional MFH technologies include: (1) analog MFHbased on RoF technology, which is described further below with respectto FIGS. 2A-B; and (2) digital MFH based on CPRI, which is describedfurther below with respect to FIGS. 3A-B.

FIG. 2A is a schematic illustration of a conventional analog MFH network200. MFH network 200 includes at least one BBU 202 in operablecommunication with an RRH 204 over a transport medium 206 (e.g., anoptical fiber). BBU 202 includes a baseband processing layer 208, anintermediate frequency (IF) up-conversion layer 210, a frequency domainmultiplexer (FDM) 212, and an electrical-optical (E/O) interface 214. Ina similar manner, RRH 204 includes a radio frequency (RF) front end 216,an RF up-conversion layer 218, a bandpass filter (BPF) 220, and anoptical-electrical (O/E) interface 222.

In operation of MFH network 200, BBU 202 receives digital bits from MBHnetworks (not shown in FIG. 2A). The received bits are processed bybaseband processing layer 208, which provides an OFDM signal to IFup-conversion layer 210 for synthesis and up-conversion to anintermediate frequency. Different wireless services are then multiplexedby FDM 212 in the frequency domain, and finally transmitted through E/Ointerface 214 to RRH 204 over an analog fiber link of transport medium206. At RRH 204, after O/E interface 222, the different services areseparated by bandpass filter(s) 220, and then up-converted by RFup-converter 218 to radio frequencies for wireless emission. Since thesewireless services are carried on different intermediate frequencies(IFs) during fiber propagation, this operation is also referred to asintermediate frequency over fiber (IFoF).

FIG. 2B is a schematic illustration of a conventional analog MFH link224 for network 200, FIG. 2A. In an exemplary embodiment, MFH 224represents a system implementation of an analog MFH link based onRoF/IFoF technology, and includes a plurality of transmitters 226 (e.g.,corresponding to a respective BBU 202) configured to transmit aplurality of respective signals 228 over link 206. Signals 228 areaggregated by FDM 212 prior to transmission over fiber 206 by E/Ointerface 214. The aggregated signals 228 are received by O/E interface222, which provides signals 228 to respective receivers 230 (e.g., of arespective RRH 204). It can be seen from the embodiment depicted in FIG.2B that the respective RF devices include mixers 232 and localoscillators 234, for both BBUs 202 and RRHs 204, for IF up-conversionand RF up-conversion, respectively. In this embodiment, transmitters 226are depicted to illustrate the IF up-conversion.

Due to its high spectral efficiency, simple equalization in thefrequency domain, and robustness against inter-symbol interference(ISI), orthogonal frequency-division multiplexing (OFDM) has beenadopted by most RATs, including WiMAX, Wi-Fi (802.11), WiGig (802.11ad),4G-LTE (3GPP), and 5G-NR. However, OFDM signals are vulnerable tononlinear impairments due to their continuously varying envelope andhigh peak-to-average ratio (PAPR). Therefore, it has become increasinglydifficult to support high order modulation formats (e.g., >256QAM) usingOFDM over MFH networks. To transmit the higher order formats required byLTE and 5G-NR signals without nonlinear distortions, digital MFHnetworks based digitization interfaces, such as CPRI, has been proposedand implemented. A digital MFH network is described below with respectto FIGS. 3A-B.

FIG. 3A is a schematic illustration of a conventional digital MFHnetwork 300. Digital MFH network 300 is similar to analog MFH network200, FIG. 2, in many respects, and includes at least one BBU 302 inoperable communication with an RRH 304 over a transport medium 306(e.g., an optical fiber). Network 300 differs from network 200 though,in that network 300 transmits mobile services using digital waveformsover medium 206, which is implemented by the digitization interface ofCPRI. BBU 302 includes a baseband processing layer 308, a Nyquistanalog-to-digital converter (ADC) 310, a first time divisionmultiplexer/demultiplexer (TDM) 312, and an electrical-optical (E/O)interface 314. In a similar manner, RRH 304 includes an RF front end316, an RF up-converter 318, a Nyquist digital-to-analog converter (DAC)320, a second TDM 322, and an optical-electrical (O/E) interface 324.

FIG. 3B is a schematic illustration of a conventional digital MFH link326 for network 300, FIG. 3A. In an exemplary embodiment, MFH 326includes a plurality of transmitters 328 (e.g., corresponding to arespective BBU 302) configured to transmit a plurality of respective bitstreams 330 over fiber link 306. Operation of network 300 thereforediffers from that of network 200, in that, after baseband processing(e.g., by baseband processing layer 308), the waveforms of basebandsignals from processor 308 are digitized into bits 330 by Nyquist ADC310. The digitized bits 330 are then transported to respective receivers332 (e.g., of a respective RRH 304) over a digital fiber link (e.g.,transport medium 306) based on mature optical intensitymodulation-direct detection (IM-DD) technology. In the configurationdepicted in FIG. 3B, the waveforms of the in-phase (I) and quadrature(Q) components of each wireless service are sampled and quantizedseparately, and the bits 330 from I/Q components of the differentservices are multiplexed in the time by first TDM 312. At the respectiveRRHs 304, after time division de-multiplexing by second TDM 322, NyquistDAC 320 recovers the IQ waveforms from received bits 334, which are thenup-converted by RF up-converter 318 to RF frequencies and fed to RFfront end 316.

Thus, when compared with analog MFH network 200 based on RoF/IFoFtechnology, digital MFH network 300 demonstrates an improved resilienceagainst nonlinear impairments, and may be implemented by existingdigital fiber links, such as, for example, a passive optical network(PON). However, these conventional digital MFH networks suffer from thefact that CPRI has a significantly low spectral efficiency, and may onlyaccommodate few narrowband RATs, such as UMTS (CPRI v1 and v2), WiMAX(v3), LTE (v4), and GSM (v5). Additionally, because CPRI uses TDMs toaggregate services, time synchronization is an additional challenge tothe coexistence of multiple RATs with different clock rates. With thelow spectral efficiency and the lack of support to Wi-Fi and 5G-NR, CPRIhas proven to be a technically-infeasible and cost-prohibitivedigitization interface for 5G heterogeneous MFH networks. Accordingly,it is desirable to develop more universal digitization techniques thatenable cost-effective carrier aggregation of multiple RATs (multi-RATs)in the next generation heterogeneous MFH networks.

BRIEF SUMMARY

In an embodiment, a digital mobile fronthaul (MFH) network includes abaseband processing unit (BBU) having a digitization interfaceconfigured to digitize, using delta-sigma digitization, at least onewireless service for at least one radio access technology. The networkfurther includes a transport medium in operable communication with theBBU. The transport medium is configured to transmit a delta-sigmadigitized wireless service from the BBU. The network further includes aremote radio head (RRH) configured to operably receive the delta-sigmadigitized wireless service from the BBU over the transport medium.

In an embodiment, a method for performing delta-sigma digitization of anaggregated signal is provided. The aggregated signal includes aplurality of different signal bands from a communication network. Themethod includes steps of oversampling the aggregated signal at rateequal to an oversampling rate times the Nyquist sampling rate togenerate an oversampled signal and quantization noise, noise shaping theoversampled signal to push the quantization noise into out-of-bandfrequency spectra corresponding to respective spectral portions betweenthe plurality of different signal bands, and filtering the noise shapedsignal to remove the out-of-band quantization noise from the pluralityof different signal bands.

In an embodiment, a baseband processing unit includes a basebandprocessor configured to receive a plurality of component carriers of aradio access technology wireless service, and a delta-sigma digitizationinterface configured to digitize at least one carrier signal of theplurality of component carriers into a digitized bit stream, fortransport over a transport medium, by (i) oversampling the at least onecarrier signal, (ii) quantizing the oversampled carrier signal into thedigitized bit stream using two or fewer quantization bits.

In an embodiment, a method for performing delta-sigma analog-to-digitalconversion (ADC) of a plurality of component carriers is provided. Themethod includes steps of obtaining a data rate of a selectedcommunication specification, selecting a quantity of the plurality ofcomponent carriers and corresponding modulation formats according to theobtained data rate, determining a signal-to-noise ratio for the selectedquantity of component carriers based on error vector magnitude valuescompatible with the selected communication specification, calculating anumber of quantization bits and a noise transfer function according tothe number of quantization bits, and quantizing the plurality ofcomponent carriers into a digitized bit stream according to the numberof quantization bits and the noise transfer function.

In an embodiment, a delta-sigma digitization interface is provided formodulating an input analog carrier signal into a digitized bit stream.The interface includes a sampling unit configured to sample the inputanalog carrier signal at a predetermined sampling rate to produce asampled analog signal, a delta-sigma analog-to-digital converterconfigured to quantize the sampled analog signal into the digitized bitstream according to a predetermined number of quantization bits, and anoutput port for transmitting the digitized bit stream to a transportmedium.

In an embodiment, a communication system is provided. The communicationsystem includes a core network, a central unit in operable communicationwith the core network, at least one distributed unit in operablecommunication with the central unit, at least one radio resource unit inoperable communication with the at least one distributed unit over anext generation fronthaul interface split option from the at least onedistributed unit. The at least one distributed unit is different fromthe central unit.

In an embodiment, a delta-sigma digitization interface is provided formodulating an input analog carrier signal into a digitized bit stream.The interface includes a sampling unit configured to sample the inputanalog carrier signal at a predetermined sampling rate to produce asampled analog signal, a segmentation unit configured to segment thesampled analog signal into a plurality of separate data pipelines, adelta-sigma analog-to-digital converter configured to individuallyquantize a respective signal segment contained within each of theplurality of data pipelines into a digitized bit stream segmentaccording to a predetermined number of quantization bits, a cascadingunit configured to combine the respective quantized signal segments intoa single digitized output stream, and an output port for transmittingthe single digitized output stream to a transport medium as thedigitized bit stream.

In an embodiment, a method is provided for optimizing a delta-sigmaanalog-to-digital converter (ADC) architecture for a field programmablegate array (FPGA). The method includes steps of simulating a performanceof the delta-sigma ADC according to a first floating-point calculationusing floating-point coefficients of the delta-sigma ADC, approximatingkey fixed-point coefficients from the floating-point coefficients,performing a second floating-point calculation of the delta-sigma ADCperformance using the approximated key fixed-point coefficients,performing a first fixed-point calculation of the delta-sigma ADCperformance for a continuous input data stream using transformedfixed-point coefficients obtained from performance of the secondfloating-point calculation, and performing a second fixed-pointcalculation of the delta-sigma ADC performance. The continuous inputdata stream is segmented into a plurality of separate data blocks, andthe second fixed-point calculation is individually performed on eachseparate segmented data block. The method further includes a step ofevaluating performance of the FPGA having a logical structure based onthe performance of the second fixed-point calculation individuallyperformed on each of the plurality of separate data blocks.

BRIEF DESCRIPTION OF THE DRAWINGS

These and other features, aspects, and advantages of the presentdisclosure will become better understood when the following detaileddescription is read with reference to the accompanying drawings in whichlike characters represent like parts throughout the drawings, wherein:

FIG. 1 is a schematic illustration of a conventional access networkarchitecture.

FIG. 2A is a schematic illustration of a conventional analog mobilefronthaul network.

FIG. 2B is a schematic illustration of a conventional analog mobilefronthaul link for the network depicted in FIG. 2A.

FIG. 3A is a schematic illustration of a conventional digital mobilefronthaul network.

FIG. 3B is a schematic illustration of a conventional digital mobilefronthaul link for the network depicted in FIG. 3A.

FIG. 4A is a schematic illustration of a digital mobile fronthaulnetwork according to an embodiment of the present disclosure.

FIG. 4B is a schematic illustration of a digital mobile fronthaul linkfor the network depicted in FIG. 4A.

FIG. 5 is a graphical illustration depicting a conventional digitizationprocess.

FIGS. 6A-C are graphical illustrations depicting a digitization processaccording to an embodiment of the present disclosure.

FIGS. 7A-C are graphical illustrations depicting respective applicationsof the digitization process depicted in FIGS. 6A-C.

FIG. 8 is a schematic illustration of a mobile fronthaul linkimplementing wavelength division multiplexing, according to anembodiment of the present disclosure.

FIG. 9 is a schematic illustration of a mobile fronthaul linkimplementing power division multiplexing, according to an embodiment ofthe present disclosure.

FIG. 10 is a graphical illustration depicting an operating principle ofthe link depicted in FIG. 9.

FIGS. 11A-D are graphical illustrations depicting a digitization processaccording to an embodiment of the present disclosure.

FIG. 12 is a flow diagram for a digitization process according to anembodiment of the present disclosure.

FIG. 13A is a schematic illustration of a filter according to anembodiment of the present disclosure.

FIG. 13B is a graphical illustration depicting an I-Q plot for a noisetransfer function for the filter depicted in FIG. 13A.

FIG. 13C is a graphical illustration depicting a frequency response ofthe noise transfer function for the filter depicted in FIG. 13A.

FIG. 14A is a graphical illustration depicting a spectrum plot accordingto an embodiment of the present disclosure.

FIG. 14B is a graphical illustration depicting a close-up view of thecarrier spectrum portion depicted in FIG. 14A.

FIG. 15A is a graphical illustration depicting error vector magnitudesfor the carriers depicted in FIG. 14B.

FIG. 15B is a graphical illustration of constellation plots for bestcase and worst case scenarios for the carriers depicted in FIG. 15A.

FIG. 16A is a schematic illustration of a filter according to anembodiment of the present disclosure.

FIG. 16B is a graphical illustration depicting an I-Q plot for a noisetransfer function for the filter depicted in FIG. 16A.

FIG. 16C is a graphical illustration depicting a frequency response ofthe noise transfer function for the filter depicted in FIG. 16A.

FIG. 17A is a graphical illustration depicting a spectrum plot accordingto an embodiment of the present disclosure.

FIG. 17B is a graphical illustration depicting a close-up view of thecarrier spectrum portion depicted in FIG. 17A.

FIG. 18A is a graphical illustration depicting error vector magnitudesfor the carriers depicted in FIG. 17B.

FIG. 18B is a graphical illustration of constellation plots for bestcase and worst case scenarios for the carriers depicted in FIG. 18A.

FIG. 19A is a graphical illustration depicting a spectrum plot accordingto an embodiment of the present disclosure.

FIG. 19B is a graphical illustration depicting a close-up view of thecarrier spectrum portion depicted in FIG. 19A.

FIG. 20A is a graphical illustration depicting error vector magnitudesfor the carriers depicted in FIG. 19B.

FIG. 20B is a graphical illustration of constellation plots for bestcase and worst case scenarios for the carriers depicted in FIG. 20A.

FIG. 21A is a graphical illustration depicting an I-Q plot for a noisetransfer function according to an embodiment of the present disclosure.

FIG. 21B is a graphical illustration depicting a frequency response ofthe noise transfer function for the I-Q plot depicted in FIG. 21A.

FIG. 22A is a graphical illustration depicting a spectrum plot accordingto an embodiment of the present disclosure.

FIG. 22B is a graphical illustration depicting a close-up view of thecarrier spectrum portion depicted in FIG. 22A.

FIG. 23A is a graphical illustration depicting error vector magnitudesfor the carriers depicted in FIG. 22B.

FIG. 23B is a graphical illustration of constellation plots for bestcase and worst case scenarios for the carriers depicted in FIG. 23A.

FIG. 24A is a graphical illustration depicting a spectrum plot accordingto an embodiment of the present disclosure.

FIG. 24B is a graphical illustration depicting a close-up view of thecarrier spectrum portion depicted in FIG. 24A.

FIG. 25A is a graphical illustration depicting error vector magnitudesfor the carriers depicted in FIG. 24B.

FIG. 25B is a graphical illustration of constellation plots for bestcase and worst case scenarios for the carriers depicted in FIG. 25A.

FIG. 26 is a graphical illustration of a comparative summary plot ofdelta-sigma radio frequency sampling rates taken against conversionbandwidths.

FIG. 27 is a schematic illustration of an access network architectureaccording to an embodiment of the present disclosure.

FIG. 28 is a schematic illustration of a radio-over-fiber link accordingto an embodiment of the present disclosure.

FIG. 29 is a schematic illustration of a system architecture accordingto an embodiment of the present disclosure.

FIG. 30A is a graphical illustration depicting a power spectral densityplot for an exemplary carrier.

FIG. 30B is a graphical illustration depicting a plot of error vectormagnitude against received optical power for the carrier depicted inFIG. 30A.

FIG. 30C is a graphical illustration depicting a post-transmissionconstellation plot for the carrier depicted in FIG. 30A.

FIG. 31A is a graphical illustration depicting a power spectral densityplot for an exemplary pair of carriers.

FIG. 31B is a graphical illustration depicting a plot of error vectormagnitudes against received optical power for the pair of carriersdepicted in FIG. 31A.

FIGS. 32A-B are graphical illustrations depicting post-transmissionconstellation plots for the carriers depicted in FIG. 31A.

FIG. 33A is a graphical illustration depicting a power spectral densityplot for an exemplary set of carriers.

FIG. 33B is a graphical illustration depicting a plot of error vectormagnitudes according to the respective carrier number of the set ofcarriers depicted in FIG. 33A.

FIG. 34A is a graphical illustration depicting a power spectral densityplot for an alternative set of carriers.

FIG. 34B is a graphical illustration depicting a plot of error vectormagnitudes according to the respective carrier number of the set ofcarriers depicted in FIG. 34A.

FIG. 35 is a schematic illustration of a parallel quantizationanalog-to-digital converter architecture.

FIG. 36 is a graphical illustration depicting an operating principle ofa delta sigma digitization process.

FIG. 37 is a schematic illustration of a delta-sigma analog-to-digitalconverter feedback architecture.

FIG. 38 is a schematic illustration of a pipeline architecture for adelta sigma digitization process.

FIG. 39 is a flow diagram of an input state process for the pipelinearchitecture depicted in FIG. 38.

FIG. 40 is a timing diagram for operation of the input buffer depictedin FIG. 38.

FIG. 41 is a flow diagram of an output state process for the pipelinearchitecture depicted in FIG. 38.

FIG. 42 is a timing diagram for operation of the output buffer depictedin FIG. 38.

FIG. 43 is a flow diagram for a fixed point coefficient implementationprocess.

FIG. 44 is a schematic illustration of an exemplary testbed.

Unless otherwise indicated, the drawings provided herein are meant toillustrate features of embodiments of this disclosure. These featuresare believed to be applicable in a wide variety of systems including oneor more embodiments of this disclosure. As such, the drawings are notmeant to include all conventional features known by those of ordinaryskill in the art to be required for the practice of the embodimentsdisclosed herein.

DETAILED DESCRIPTION

In the following specification and the claims, reference will be made toa number of terms, which shall be defined to have the followingmeanings.

The singular forms “a,” “an,” and “the” include plural references unlessthe context clearly dictates otherwise.

“Optional” or “optionally” means that the subsequently described eventor circumstance may or may not occur, and that the description includesinstances where the event occurs and instances where it does not.

Approximating language, as used herein throughout the specification andclaims, may be applied to modify any quantitative representation thatcould permissibly vary without resulting in a change in the basicfunction to which it is related. Accordingly, a value modified by a termor terms, such as “about,” “approximately,” and “substantially,” are notto be limited to the precise value specified. In at least someinstances, the approximating language may correspond to the precision ofan instrument for measuring the value. Here and throughout thespecification and claims, range limitations may be combined and/orinterchanged; such ranges are identified and include all the sub-rangescontained therein unless context or language indicates otherwise.

As used herein, the terms “processor” and “computer” and related terms,e.g., “processing device”, “computing device”, and “controller” are notlimited to just those integrated circuits referred to in the art as acomputer, but broadly refers to a microcontroller, a microcomputer, aprogrammable logic controller (PLC), an application specific integratedcircuit (ASIC), and other programmable circuits, and these terms areused interchangeably herein. In the embodiments described herein, memorymay include, but is not limited to, a computer-readable medium, such asa random access memory (RAM), and a computer-readable non-volatilemedium, such as flash memory. Alternatively, a floppy disk, a compactdisc—read only memory (CD-ROM), a magneto-optical disk (MOD), and/or adigital versatile disc (DVD) may also be used. Also, in the embodimentsdescribed herein, additional input channels may be, but are not limitedto, computer peripherals associated with an operator interface such as amouse and a keyboard. Alternatively, other computer peripherals may alsobe used that may include, for example, but not be limited to, a scanner.Furthermore, in the exemplary embodiment, additional output channels mayinclude, but not be limited to, an operator interface monitor.

Further, as used herein, the terms “software” and “firmware” areinterchangeable, and include computer program storage in memory forexecution by personal computers, workstations, clients, and servers.

As used herein, the term “non-transitory computer-readable media” isintended to be representative of any tangible computer-based deviceimplemented in any method or technology for short-term and long-termstorage of information, such as, computer-readable instructions, datastructures, program modules and sub-modules, or other data in anydevice. Therefore, the methods described herein may be encoded asexecutable instructions embodied in a tangible, non-transitory, computerreadable medium, including, without limitation, a storage device and amemory device. Such instructions, when executed by a processor, causethe processor to perform at least a portion of the methods describedherein. Moreover, as used herein, the term “non-transitorycomputer-readable media” includes all tangible, computer-readable media,including, without limitation, non-transitory computer storage devices,including, without limitation, volatile and nonvolatile media, andremovable and non-removable media such as a firmware, physical andvirtual storage, CD-ROMs, DVDs, and any other digital source such as anetwork or the Internet, as well as yet to be developed digital means,with the sole exception being a transitory, propagating signal.

Furthermore, as used herein, the term “real-time” refers to at least oneof the time of occurrence of the associated events, the time ofmeasurement and collection of predetermined data, the time for acomputing device (e.g., a processor) to process the data, and the timeof a system response to the events and the environment. In theembodiments described herein, these activities and events occursubstantially instantaneously.

According to the embodiments described herein, multiband delta-sigmadigitization systems and methods enable carrier aggregation ofmulti-RATs in next generation heterogeneous MFH networks. The presentmultiband delta-sigma ADC techniques allow different RAT technologies,such as, 4G-LTE, Wi-Fi, and 5G-NR signals, to be aggregated anddelivered together with shared MFH networks. The present embodimentsadvantageously enable the aggregation of heterogeneous wireless servicesfrom multi-RATs in the frequency domain, and then the digitization ofthe aggregated services simultaneously in an “as is” manner, that is,without frequency conversion.

These advantageous configurations are thus able to circumvent clock ratecompatibility and time synchronization problems arising from multi-RATcoexistence, while also eliminating the need of DAC and RF devices atremote cell cites (e.g., RRHs), thereby further enabling a low-cost,all-analog implementation of RRHs where desired. The present embodimentsfurther significantly reduce the cost and complexity of 5G small cells,while also facilitating large-scale dense deployment of heterogeneous 5GMFH networks. The present systems and methods further provide aninnovative digitization interface advantageously replaces CPRI, therebyrealizing a significantly higher spectral efficiency, while alsooffering improved compatibility for multi-RAT coexistence in 5Gheterogeneous MFH networks.

FIG. 4A is a schematic illustration of a digital MFH network 400.Network 400 is similar to networks 200, FIG. 2A, 300, FIG. 3A in anumber of respects, but represents an improved digitization interfacefor implementing multiband delta-sigma digitization. MFH network 400includes at least one BBU 402 in operable communication with an RRH 404over a transport medium 406 (e.g., an optical fiber). BBU 402 includes abaseband processor 408, an RF up-converter 410, a delta-sigma ADC 412,and an E/O interface 414. In a similar manner, RRH 404 includes an RFfront end 416, a BPF 418, and an O/E interface 420.

FIG. 4B is a schematic illustration of a digital MFH link 422 fornetwork 400, FIG. 4A. In exemplary operation of link 422, at respectivetransmitters 424 (e.g., of respective BBUs 402), after basebandprocessing by baseband processor 408, a plurality of various wirelessservices 426 (e.g., from different RATs) are up-converted by RFup-converter 410 to RF frequencies, and then aggregated in the frequencydomain by an FDM 428. The wireless signals of aggregated services 426are then digitized by delta-sigma ADC 412 (e.g., a multiband delta-sigmaADC) to generate a digitized delta-sigma data stream 430. In theexemplary embodiment, delta-sigma ADC 412 digitizes multibandsignals/services 426 simultaneously. Unlike Nyquist ADC techniques usedin CPRI (e.g., by Nyquist ADC 310, FIG. 3), which only digitize basebandsignals, multiband delta-sigma ADC 412 is advantageously able todigitize wireless services 426 in an “as is” manner, without the need offrequency down-conversion.

In the exemplary embodiment depicted in FIG. 4B, transmitters 424 aredepicted, for example, to illustrate the RF up-conversion of I and Qcomponents of different wireless services. Further to this example, inthis architecture, respective RF devices, including without limitationlocal oscillators 432, mixers 434, and delta-sigma ADCs 412 may all beadvantageously centralized in BBU 402, whereas only BPFs 418 andrespective antennas of RF front ends 416 are needed in RRHs 404. Thissimplified design enables a DAC-free and RF-free RRH, which may befurther advantageously implemented by essentially all relevant analogdevices. This configuration is particularly advantageous with respect tothe 5G paradigm, given the wide and dense deployment of small cells.That is, an all-analog, DAC-free, RF-free architecture (i.e., accordingto FIGS. 4A-B) will significantly reduce the cost and complexity ofexisting and future RRHs.

In the embodiments depicted in FIGS. 4A-B, the digital MFH architectureis depicted to implement FDM (e.g., FDM 428) to multiplex wirelessservices (e.g., services 426), and analog BPFs (e.g., BPFs 418) toseparate the multiplexed wireless services. This configuration thusavoids the compatibility problem of different baseband chip rates forvarious RATs, and also circumvents the synchronization problemexperienced among the different services. Furthermore, the delta-sigmadigitization techniques of the present embodiments provide awaveform-agnostic interface, which not only supports OFDM, but alsoworks with other multicarrier waveforms, such as filter bankmulticarrier (FBMC), universal filtered multicarrier (UFMC), etc.

FIG. 5 is a graphical illustration depicting a conventional digitizationprocess 500. Sampling process 500 depicts the operation of aconventional Nyquist ADC used in CPRI for an analog signal 502 (shown inthe time domain). In operation, process 500 bandwidth-limits analogsignal 502 as a corresponding frequency domain signal 504 using alow-pass filter. That is, in the frequency domain, analog signal 502 isbandwidth limited to digital signal 504. After digitization,quantization noise 506 is uncorrelated with the frequency of the inputsignal, and is spread evenly over the Nyquist zone f_(s)/2. In the timedomain, process 500 performs Nyquist sampling 508 of analog signal 502(i.e., at the Nyquist frequency), and quantizes each obtained sample bymultiple quantization bits to produce multi-bit quantization signal 510.

Since the quantization noise of a Nyquist ADC is approximately Gaussian,as well as uniformly spread over the Nyquist zone, a very large numberof quantization bits are needed to ensure the signal-to-noise ratio(SNR) (e.g., CNR or MER) of the resulting digitized signals 510. Such alarge number of required quantization bits leads to low spectralefficiency, as well as a data rate bottleneck of MFH networks.

More specifically, as depicted in FIG. 5, in conventional CPRI NyquistADC, each LTE carrier is digitized individually by a Nyquist ADC having,for example, a sampling rate of 30.72 MSa/s. For each sample, 15quantization bits and one control bit (16 bits total) are used torepresent the analog amplitude. The quantization noise (e.g.,quantization noise 506) of a Nyquist ADC is evenly distributed in theNyquist zone in the frequency domain, which can be approximated byGaussian white noise.

To reduce the quantization noise and increase the SNR of digitizedsignal, CPRI requires a large number of quantization bits, therebyresulting in the low spectral efficiency and significant bandwidth afterdigitization, which render CPRI the data rate bottleneck of digital MFHnetworks. In the case of line coding of 8b/10b, CPRI will consume up to30.72 MSa/s *16 bitSa*10/8*2=1.23 Gb/s of MFH capacity for each 20 MHzLTE carrier. Within a 10-Gb/s PON link, for example, CPRI is onlycapable of accommodating eight LTE carriers.

Additionally, CPRI is known to operate at a fixed chip rate of 3.84 MHz,and to only support a limited number of RATs, such as UMTS (CPRI v1 andv2), WiMAX (v3), LTE (v4), and GSM (v5). Given the different clock ratesof various RATs, time synchronization remains a problem for multi-RATcoexistence. Moreover, the low spectral efficiency and inability tosupport to Wi-Fi and 5G-NR render CPRI technically lacking andcost-prohibitive as a digitization interface for 5 G heterogeneous MFHnetworks. These drawbacks are solved through implementation of thefollowing innovative processes.

FIGS. 6A-C are graphical illustrations depicting a digitization process600. In an exemplary embodiment, process 600 demonstrates an operationalprinciple of the multiband delta-sigma ADC techniques described herein,and may be executed by a processor (not shown in FIGS. 6A-C) in one ormore BBUs. More specifically, FIG. 6A depicts an oversampling subprocess602 of process 600, FIG. 6B depicts a noise shaping subprocess 604 ofprocess 600, and FIG. 6C depicts a filtering subprocess 606 of process600.

In an exemplary embodiment of oversampling subprocess 602, quantizationnoise 608 is spread over a relatively wide Nyquist zone (e.g., theoversampling rate (OSR) times the Nyquist sampling rate f_(s)/2, orOSR*f_(s)/2). In this example, because the quantization number islimited to one or two quantization bits, namely, one-bit quantization610 (e.g., a binary, or on-off keying (OOK) signal) or two-bitquantization 612 (e.g., a PAM4 signal), quantization noise 608 issignificant. In the exemplary embodiment depicted in FIGS. 6A-C, threenon-contiguous signal bands 614 of wireless services are aggregatedtogether. In some embodiments, signal bands 614 come from the same RAT(e.g., intra-RAT carrier aggregation). In other embodiments, signalbands 614 come from different RATs (e.g., inter-RAT carrieraggregation). Oversampling subprocess 602 and thus results in anoversampled analog signal 616.

In an exemplary embodiment of noise shaping subprocess 604, quantizationnoise 608′ is pushed out of the signal bands 614, thereby separatingsignals from noise in the frequency domain. In this example ofsubprocess 604, the respective spectra of signal bands 614 are notmodified during the operation of digitization process 600. In anexemplary embodiment of filtering subprocess 606, bandpass filters 616are respectively applied to signal bands 614 to substantially eliminatethe out-of-band (OOB) noise (e.g., quantization noise 608′) and therebyenable retrieval of an output signal 618 closely approximating theoriginal analog waveform.

This advantageous technique thus represents a significant improvementover the conventional Nyquist ADC techniques described above withrespect to FIG. 5. More particularly, through implementation of amultiband delta-sigma ADC according to the operational principles ofprocess 600, the known shortcomings of CPRI may be successfullycircumvented. For example, instead of the large number of quantizationbits required by conventional CPRI techniques, the present delta-sigmaADC embodiments successfully “trade” quantization bits for the samplingrates described herein. The present techniques thus exploit a highsampling rate, but only require relatively few (i.e., one or two)quantization bits to be fully implemented.

In the exemplary embodiments depicted in FIGS. 6A-C, the OOBquantization noise (e.g., quantization noise 608′) is added by thedelta-sigma ADC (not shown in FIGS. 6A-C), and which converts theoriginal signal waveform from analog to digital. At the RRH, theoriginal analog waveform (e.g., output signal 618) may then be easilyretrieved once the quantization noise is eliminated by filtering (e.g.,filtering subprocess 606). From the noise shaping technique of noiseshaping subprocess 604 though, the retrieved analog signal may have anuneven noise floor. Accordingly, in an embodiment, the noise shapingtechnique may be configured to exploit a noise transfer function tocontrol the frequency distribution of quantization noise 608′, whereeach conjugate pair of zero points of the noise transfer functioncorresponds to a null point of noise. In the design of a multibanddelta-sigma ADC, one or two pairs of zeros of the noise transferfunction may be assigned to each signal band 614, depending on thebandwidth.

The operational principles of the present delta-sigma ADC may also beadvantageously interpreted in the time domain. The present delta-sigmaADC techniques have, for example, a memory effect, whereas conventionalNyquist ADC techniques have no such memory effect. Conventional NyquistADC operations quantize each sample individually and independently, andthe resultant output bits are only determined by the input amplitude forthat particular sample, which has no dependence on previous samples. Incontrast, the present delta-sigma ADC techniques are able to digitizesamples consecutively whereby a particular output bit may depend notonly on the particular input sample, but also on previous samples.

For example, in the case of a sinusoidal analog input, a one-bitdelta-sigma ADC according to the present embodiments outputs a highspeed OOK signal with a density of “1” bits, proportional to theamplitude of analog input. Thus, when the input is close to its maximumvalue, the output will include almost all “1” bits. However, when theinput is close to its minimum value, the output will include all “0”bits. Similarly, for intermediate inputs, the output will be expected tohave an equal density of “0” and “1” bits.

FIGS. 7A-C are graphical illustrations depicting respective applications700, 702, 704 of digitization process 600, FIGS. 6A-C (e.g., after noisefiltering subprocess 604). More specifically, application 700 depicts acase of intra-RAT contiguous carrier aggregation, application 702depicts a case of intra-RAT non-contiguous carrier aggregation, andapplication 704 depicts a case of heterogeneous inter-RAT carrieraggregation.

In an exemplary embodiment of application 700, a case of intra-RATcontiguous carrier aggregation may occur where wireless services 706from the same RAT are bonded together contiguously in the frequencydomain, and digitized simultaneously by a single-band delta-sigma ADC.Examples of this scenario include LTE contiguous carrier aggregation andWi-Fi channel bonding.

In an exemplary embodiment of application 702, a case of intra-RATnon-contiguous carrier aggregation may occur where wireless services 708from the same RAT are aggregated non-contiguously, and digitizedtogether by a multiband delta-sigma ADC. Examples of this scenarioinclude LTE non-contiguous carrier aggregation.

In an exemplary embodiment of application 704, a case of heterogeneousinter-RAT carrier aggregation may occur where respective wirelessservices 710, 712, 714 from different RATs (e.g., an LTE RAT for service710, a Wi-Fi RAT for service 712, and a 5G-NR RAT for service 714) areaggregated in a heterogeneous MFH network. As illustrated in thisembodiment, a waveform/RAT-agnostic digitization interface is providedthat eliminates the need for DAC and RF devices in RRHs, while alsosupporting multiband wireless services with different carrierfrequencies and bandwidths from multiple RATs, without presenting thesynchronization or compatibility problems experienced by conventionaldigitization interfaces.

In the embodiments depicted in FIGS. 7A-C, each frequency band isutilized by only one wireless service. Other application scenarios offrequency sharing, such as in the case where one frequency component isoccupied by more than one wireless signals (e.g., frequency overlapamong multiple RATs or multiple-input multiple-output (MIMO)) arecontemplated, but not illustrated in this example. Various frequencyranges of different RATs, including overlaps, are illustrated below inTable 1.

TABLE 1 RAT Wi-Fi (802.11) WiMAX LTE UWB Protocol a g n ac/ax af ah802.16 e 3GPP 802.15.3a (rel. 8) Freq. 5.15-5.875 2.4-2.497 5.15-5.875,5.15-5.875 0.054-0.698, <1 2.1-5.9 0.7-2.6 3.168-10.56 bands 2.4-2.4970.47-0.79 (GHz)

As can be seen from the information provided in Table 1, problems occuras a result of frequency reuse. As described further below with respectto FIGS. 8 and 9, respectively, the present systems and methods providefurther solutions to overcome the problems of frequency reuse based onwavelength division multiplexing (WDM) and power division multiplexing(PDM) technologies.

FIG. 8 is a schematic illustration of an MFH link 800 implementing WDM.MFH link 800 is similar in some structural respects to MFH link 400,FIG. 4, and includes a first group of transmitters 802 and a secondgroup of transmitters 804 in operational communication with a first FDM806 and a second FDM 808, respectively. Additionally, first FDM 806 andsecond FDM 808 are also in operational communication with a firstdelta-sigma ADC 810 and a second delta-sigma ADC 812, respectively. Inan exemplary embodiment of MFH link 800, multiple wireless services atthe same RF frequencies may be advantageously digitized and supported bydifferent wavelengths using WDM technology.

More particularly, digital bit streams from first and second delta-sigmaADCs 810, 812 are carried by different wavelengths λ₁ and λ_(2,)respectively, and then multiplexed by a WDM multiplexer 814 onto asingle fiber transport medium 816. In the example depicted in FIG. 8, afirst OOK₁ is carried on wavelength λ₁, which supports three wirelessservices 818 at respective frequencies of f_(RF1), f_(RF2), and f_(RF3),and a second OOK₂ is carried on wavelength λ_(2,) which supports threedifferent wireless services 820 at respective frequencies of f_(RF4),f_(RF5), and f_(RF6). Further in this example, the frequencies f_(RF2)=f_(RF5); however, the two wavelengths λ₁ and λ₂ are separated at firstRRH 822 and second RRH 824 by a WDM de-multiplexer 826. Thus, theseparate services f_(RF2) and f_(RF5) may be filtered out bycorresponding filters 828 (e.g., BPF₂ and BPF₅, respectively, in thisexample).

FIG. 9 is a schematic illustration of an MFH link 900 implementing PDM.MFH link 900 is similar to MFH link 800, FIG. 8, and includes a firstgroup of transmitters 902 and a second group of transmitters 904 inoperational communication with a first FDM 906 and a second FDM 908,respectively. Additionally, first FDM 906 and second FDM 908 are also inoperational communication with a first delta-sigma ADC 910 and a seconddelta-sigma ADC 912, respectively. In an exemplary embodiment of MFHlink 900, multiple wireless services at the same RF frequencies may beadvantageously supported by different power levels using PDM technology.

More particularly, a first digitized bit stream 914 from firstdelta-sigma ADC 910 and a second digitized bit stream 916 from seconddelta-sigma ADC 912 have different amplitudes and may be superimposed inthe power domain by a power combiner 918. That is, in MFH link 900, thetwo digitized bit streams 914, 916 of differing amplitudes aremultiplexed in the power division and synthesized to a single 4-levelpulse amplitude modulation (PAM4) signal 920. A signal 920 may then bedelivered from first and second transmitter groups 902, 904 (e.g., ofrespective BBUs) to corresponding first and second RRH groups 922, 924,respectively over a single fiber transport medium 926.

Similar to the embodiment depicted in FIG. 8, in MFH link 900, firstdigitized bit stream 914 represents an OOK₁ signal carrying wirelessservices 928 at respective frequencies of f_(RF1), f_(RF2), and f_(RF3),and second digitized bit stream 916 represents an OOK₂ signal carryingdifferent wireless services 930 at respective frequencies of f_(RF4),f_(RF5), and f_(RF6). However, in this example, the amplitude of OOK₁ istwice that of OOK₂, and thus the summation of the OOK₁ and OOK₂ signalssynthesize PAM4 signal 920 (described further below with respect to FIG.10). Also similar to the example depicted in FIG. 8, again frequenciesf_(RF2)=f_(RF5). In further operation of MFH link 900, prior toreception by first and second RRH groups 922, 924, and furtherdownstream from an O/E interface 932 (e.g., a photodetector), and OOKreceiver 934 is configured to retrieve the OOK₁ signal, and a PAM4receiver 936 is configured to retrieve the OOK₂ signal. In this example,the relatively larger offset imposed by the OOK₁ signal is removedbefore MFH link 900 is able to retrieve the relatively smaller amplitudeof the OOK₂ signal.

FIG. 10 is a graphical illustration depicting an operating principle1000 of MFH link 900, FIG. 9. In an exemplary embodiment, operatingprinciple 1000 depicts a synthesis effect of PDM using the presentdelta-sigma digitization techniques. More particularly, operatingprinciple 1000 illustrates the synthesis of PAM4 signal 920 by thesummation (e.g., by power combiner 918) of the OOK₁ signal of firstdigitized bit stream 914 and the OOK₂ signal of the second digitized bitstream 916. The amplitude ratio of OOK₁ signal and the OOK₂ signal is2:1.

According to the embodiments described herein, innovative multibanddelta-sigma digitization are provided that are advantageously capable ofsupporting heterogeneous carrier aggregations in 5G heterogeneous mobilefronthaul networks, including without limitation, 4G-LTE, Wi-Fi, and5G-NR. The advantageous systems and methods of the present embodimentsare further capable of aggregating heterogeneous wireless services inthe frequency domain, thereby avoiding the baseband clock ratecompatibility and time-synchronization problems arising from multi-RATcoexistence. The present techniques are further capable of digitizingmultiband wireless services simultaneously, in an “as is” manner,without requiring frequency conversion, and thereby eliminating the needfor DAC and RF devices at RRHs. By providing a significantly lower-costand efficient all-analog implementation capability for RRHs the presentsystems and methods are particularly useful to significantly reduce RRHcost and complexity, which will facilitate wide dense deployment of 5Gsmall cells.

The embodiments described herein further propose respective solutionsbased on wavelength/power division multiplexing (WDM/PDM) technologiesto accommodate more than one wireless service at the same frequency.These additional embodiments therefore further enable frequency sharingamong multiple RATs and MIMO deployments. Additional exemplary systemsand methods for implementing delta-sigma digitization are described inco-pending U.S. patent application Ser. No. 15/847,417, filed Dec. 19,2017, and to U.S. patent application Ser. No. 16/180,591, filed Nov. 5,2018, the disclosures of both of which are incorporated by referenceherein.

Flexible Digitization Interface

In accordance with one or more of the systems and methods describedabove, an innovative flexible digitization interface is provided. In anexemplary embodiment, the present digitization interface is based ondelta-sigma ADC, which advantageously enables on-demand provisioning ofSNR and data rates for MFH networks. By eliminating the conventional DACat the RRH, the present systems and methods are capable of significantlyreducing the cost and complexity of small cells. In particularembodiments, the present digitization interface enables an all-analogimplementation of RRHs, and is capable of handling variable samplingrates, adjustable quantization bits, and/or flexible distribution ofquantization noise. In some embodiments, the interface further utilizesnoise shaping techniques to adjust the frequency distribution ofquantization noise as needed or desired, thereby further enablingadvantageous on-demand SNR and data rate provisioning.

As described above, the rapid growth of mobile data, driven by theemerging video-intensive/bandwidth-hungry services, immersiveapplications, 5G-NR paradigm technologies (e.g., MIMO, carrieraggregation, etc.), creates significant challenges for existing opticaland wireless access networks. The embodiments described above feature aninnovative C-RAN architecture that enhances the capacity and coverage ofcellular networks and consolidates baseband signal processing andmanagement functions into a BBU pool. The exemplary architectures dividethe RANs into two segments: (1) an MBH segment from the core network tothe BBUs; and (2) a MFH segment from the BBUs to the RRHs.

However, as also described above, conventional techniques such as CPRI,despite the overprovisioning SNR, suffer from low spectral efficiencyand lack of scalability/flexibility, rendering such techniques abottleneck of digital MFH networks for 5G services. Accordingly there isa need for an improved delta-sigma digitization interface to replaceCPRI, which not only circumvents the CPRI data-rate bottleneck byimproving the spectral efficiency, but also addresses the scalabilityand flexibility problems from CPRI by advantageously providingreconfigurability and flexibility in terms of sampling rate,quantization bit number, and quantization noise distribution. Thepresent delta-sigma digitization interface thus provides for agile,on-demand SNR and data rate provisioning, while also allowing asignificantly simplified RRH design that enables all-analog, DAC-freeimplementation. Such architectural simplifications significantly reducethe cost and complexity of 5G small cells for wide deployment.

An exemplary architecture that may implement the present flexibledigitization interface is described above with respect to FIG. 4.Compared with the conventional digital MFH based on CPRI (e.g., FIG. 3),the Nyquist ADC in the BBU may be replaced by a delta-sigma ADC, and theNyquist DAC in RRH may be replaced by a BPF. At the BBU, differentmobile services are carried on IFs and multiplexed in the frequencydomain. After delta-sigma ADC, the services may be digitized into bitsand delivered to the RRH, for example, by an optical IM-DD link. At theRRH, a BPF may filter out the desired mobile service, eliminate the OOBquantization noise, and retrieve the analog waveform. This exemplaryconfiguration, where the BPF implements DAC and frequency de-multiplexerfunctions, significantly reduces the system complexity of the RRH,enables an all-analog implementation thereof, capable of handling anysampling rate or quantization bit number without synchronizationproblems. Given the wide and dense deployment of small cells in 5Gparadigm, this all-analog, DAC-free RRH design will significantly reducethe cost and complexity of small cells.

A comparison of FIG. 5 with FIGS. 6A-C, above, illustrates thedifference in operating principles between a Nyquist ADC and adelta-sigma ADC, respectively. As described above, in CPRI, each LTEcarrier is digitized individually by a Nyquist ADC with a sampling rateof 30.72 MSa/s and 15 quantization bits. For each sample, 16 bits total(i.e., 15 quantization bits and one control bit) are used to transformthe analog amplitude to digital bits. To accommodate various RATs, CPRIhas a fixed basic frame rate 3.84 MHz, and can only work at a fixedsampling rate and fixed number of quantization bits. The quantizationnoise of a Nyquist ADC is evenly distributed in the frequency domain,and therefore CPRI requires a large number of quantization bits toreduce the quantization noise and maintain a high SNR for the digitizedsignal, thereby leading to the low spectral efficiency and high databandwidth bottleneck problems.

CPRI data rate options are shown in Table 2, below. With line coding of8b/10b, CPRI consumes up to 30.72 MSa/s*16bitSa*10/8*2=1.23 Gb/s MFHcapacity for each 20 MHz LTE carrier (e.g., Option 2 in Table 2). Withina 10-Gb/s PON, only eight LTE carriers may be accommodated (e.g., Table2, Option 7). LTE carrier aggregation was initially standardized by 3GPPrelease 10, which allowed 5 component carriers, and then expanded toallow 32 CCs in 3GPP release 13. This expanded carrier aggregation mayconsume up to 40 Gb/s fronthaul capacity if digitized by CPRI, whichcannot be supported by existing optical/wireless access networks.

TABLE 2 Line LTE Option coding carrier # Examples Bit rate (Mb/s) 18b/10b 0.5 Only I or Q 491.52 × 10/8 = 614.4 2 8b/10b 1 One 20-MHz491.52 × 10/8 × 2 = LTE CC 1228.8 3 8b/10b 2 2 CA or 491.52 × 10/8 × 4 =2 × 2 MIMO 2457.6 4 8b/10b 2.5 Only I/Q, 5 CA 491.52 × 10/8 × 5 = 3072 58b/10b 4 4 × 4 MIMO 491.52 × 10/8 × 8 = or 2 CA + 4915.2 2 × 2 MIMO 68b/10b 5 5 CA 491.52 × 10/8 × 10 = 6144 7 8b/10b 8 8 × 8 MIMO 491.52 ×10/8 × 16 = or 2 CA + 9830.4 4 × 4 MIMO   7A 64b/66b  8 8 × 8 MIMO491.52 × 66/64 × 16 = or 4 CA + 8110.08 2 × 2 MIMO 8 64b/66b  10 5 CA +491.52 × 66/64 × 20 = 2 × 2 MIMO 10137.6 9 64b/66b  12 3CA + 491.52 ×66/64 × 24 = 4 × 4 MIMO 12165.12

FIGS. 11A-D are graphical illustrations depicting a digitization process1100. In an exemplary embodiment, process 1100 demonstrates anoperational principle of an alternative delta-sigma ADC techniquesaccording to the present systems and methods. Similar to process 600,FIGS. 6A-C, process 1100 may also be executed by a processor in one ormore BBUs. More specifically, FIG. 11A depicts a Nyquist samplingcondition 1102, FIG. 11B depicts an oversampling subprocess 1104 ofprocess 1100, FIG. 11C depicts a noise shaping subprocess 1106 ofprocess 1100, and FIG. 11D depicts a filtering subprocess 1108 ofprocess 1100.

Sampling condition 1102, for example, represents a case where a limitednumber of quantization bits 1110 results in significant quantizationnoise 1112 for non-contiguous aggregated wireless service signal bands1114 sampled at the Nyquist sampling rate f_(s)/2. In this case, due tothe limited number of quantization bits 1110, significant quantizationnoise is present if the analog signal is sampled at its Nyquist rate. Incontrast, in an exemplary embodiment of oversampling subprocess 1104,oversampling extends the Nyquist zone, and quantization noise 1116 isspread over a relatively wider frequency range/wide Nyquist zone (e.g.,the oversampling rate (OSR) times the Nyquist sampling rate f_(s)/2, orOSR*f_(s)/2). Similar to the embodiments described above, oversamplingsubprocess 1104 extends the Nyquist zone, spreads quantization noise1116 over a wider frequency range, and thereby results in an oversampledanalog signal 1118 where in-band SNR is improved.

In an exemplary embodiment of noise shaping subprocess 1106,quantization noise 1116′ is pushed out of the signal bands 1114′,thereby separating signals from noise in the frequency domain. In thisexample of subprocess 1106, the respective spectra of signal bands 1114′are not modified during the operation of process 1100. In an exemplaryembodiment of filtering subprocess 1108, a BPF 1118 is applied to signalbands 1114′ to substantially eliminate the OOB noise, and also enableretrieval of an output signal 1120 closely approximating the originalanalog waveform.

Process 1100 therefore advantageously circumvents the data ratebottleneck and flexibility issues of CPRI through the innovativeflexible digitization interface described above, which is based ondelta-sigma ADC. According to the techniques described herein, insteadof digitizing each LTE carrier individually, the carriers may first bemultiplexed in the frequency domain, and then digitized by a delta-sigmaADC. Unlike the Nyquist ADC, which uses many quantization bits, thepresent delta-sigma ADC techniques trade quantization bits for samplingrate, exploiting a high sampling rate, but only one or two quantizationbits.

According to the present delta-sigma ADC systems and methods, the signalwaveforms are transformed from analog to digital by adding quantizationnoise without changing the spectrum of original analog signal.Therefore, to retrieve the analog waveform, the present delta-sigmadigitization processing does not require a DAC, and may instead utilizea BPF to filter out the desired signal (e.g., FIG. 11D), which greatlysimplifies the architectural design of the system. Once OOB noise iseliminated, the analog waveform is retrieved. Accordingly, a BPF (e.g.,BPF 1118, FIG. 11D) may replace the Nyquist DAC (e.g., Nyquist DAC 320,FIG. 3A), and further perform frequency de-multiplexing functions inadditions to the DAC functions, thereby also replacing a de-multiplexer(e.g., time domain de-multiplexer 322, FIG. 3A). In some cases, theretrieved analog signal may have an uneven noise floor from noiseshaping.

In some embodiments, the present delta-sigma ADC techniques may alsooperate in the time domain. One key difference between Nyquist anddelta-sigma ADC, for example, is that Nyquist ADC has no memory effect,whereas delta-sigma ADC does have a memory effect. As described above,Nyquist ADC quantizes each sample individually and independently, i.e.,current output bits are only determined by the current sample, but haveno relevance to previous samples. Delta-sigma ADC, on the other hand,digitizes samples consecutively, i.e., the current output bit may dependon not only the current input sample, but also on previous samples. Forexample, with a sinusoidal analog input, a one-bit delta-sigma ADCoutputs an OOK signal with a density of “1” bits proportional to theinput analog amplitude. When the input is close to its maximum, theoutput contains almost all “1” bits; when the input is close to aminimum value, the output contains all “0” bits (e.g., bits 1110, FIG.11C). For intermediate inputs, the output will have an equal density of“0” and “1” bits.

The present embodiments thus concentrate a significant quantity ofdigital signal processing (DSP) capabilities into the BBU, and enable aDAC-free, all analog implementation of the RRHs, which not only reducesthe cost and complexity of RRHs significantly, but also makes flexibledigitization possible. With an analog RRH, the sampling rate, the numberof quantization bits, and the frequency distribution of quantizationnoise may be flexibly reconfigured according to the required SNR anddata rate without experiencing synchronization problems.

As described further below with respect to FIGS. 12-25B, a digitizationprocess (i.e., FIG. 12) is provided for several exemplary implementationscenarios (i.e., FIGS. 13A-25B) that demonstrate the flexibility andreconfigurability of the present delta-sigma digitization interface foron-demand SNR provisioning.

More specifically, five exemplary scenarios are described andillustrated below, which demonstrate the reconfigurability of thepresent delta-sigma digitization interface in terms of sampling rate,quantization bits, and noise distribution. The flexibility of thepresent digitization interface is described with respect to enhancedcapabilities for on-demand provisioning of SNR, and also of data rate(e.g., for LTE). In some of the examples described below, the SNR isevaluated in terms of error vector magnitude (EVM). Exemplary 3GPP EVMrequirements for different modulation formats are listed in Table 3,below.

TABLE 3 Modulation QPSK 16QAM 64QAM 256QAM 1024QAM* EVM (%) 17.5 12.5 83.5 1

With respect to Table 3, it is noted that the 3GPP specification onlyincludes modulation formats up to 256QAM, and therefore does not includean EVM for the 1024QAM modulation format. Accordingly, an EVM value of1% it is included in Table 3 as a tentative criterion.

The five separate exemplary implementation scenarios are illustrated inTable 4, below. These exemplary implementation scenarios demonstrate theflexibility of the present delta-sigma digitization techniques foron-demand provisioning of SNR and LTE data rates, in terms of ADC order,sampling rate, quantization bits, and noise distribution. For each Caselisted in Table 4, different modulation formats are assigned todifferent carriers according to the respective SNR and EVM requirementsspecified by 3GPP for the particular modulation order. Accordingly,several different data rate options may be provisioned depending on thedistribution of quantization noise.

TABLE 4 Case I II III IV V Order 2 4 4 4 4 Bits 1 1 2 1 2 Digitalwaveform OOK OOK PAM4 OOK PAM4 MFH capacity (Gb/s) 10 10 20 10 20 LTEcarriers 32 32 32 37 37 MFH capacity per LTE 312.5 312.5 625 270.27540.54 carrier (Mb/s) SE Improvement than 3.93 3.93 1.97 4.55 2.27 CPRIModulation 64QAM × 18 256QAM × 16 1024QAM × 10 256QAM × 12 1024QAM × 816QAM × 14 64QAM × 16 256QAM × 22 64QAM × 25 256QAM × 29 Raw LTE datarate (Gb/s) 2.952 4.032 4.968 4.428 5.616 Digitization efficiency 0.300.40 0.25 0.44 0.28 Comments Low cost High SE High SNR Highest High SNRLow SNR High data rate SE High data rate Low data rate FIGS. 13A-15B16A-18B 19A-20B 21A-23B 24A-25B

In the first Case I example, which is based on a second-order one-bitdelta-sigma ADC, a relatively simple, low-cost MFH solution is provided,and which exhibits a limited SNR and low data rate, and which is capableof digitizing 32 carriers with low modulation formats (e.g., 64QAM and16QAM). This exemplary embodiment is described further below withrespect to FIGS. 13-15.

In the Case II example, the order of delta-sigma ADC is upgraded fromtwo to four, which significantly reduces the quantization noise.Accordingly, higher SNR and modulation formats may be supported toprovision a larger data rate. This exemplary embodiment is describedfurther below with respect to FIGS. 16-18. In the Case III example, thequantization bit number is increased from one to two, which furtherreduces the quantization noise. Accordingly, even higher SNR andmodulation formats may be supported. This exemplary embodiment isdescribed further below with respect to FIGS. 19-20.

As listed in Table 4, the Case IV (described further below with respectto FIGS. 21-23) and Case V(described further below with respect to FIGS.21-23) examples may utilize a fourth-order ADC similarly to an ADCimplemented with respect to the Case II and Case III examples, but adifferent noise distribution. That is, the frequency distribution ofquantization noise in the Case II and Case III example scenarios istuned to maximize the SNR for 32 carriers. In contrast, the Case IV andCase V example scenarios may implement the same fourth-order ADC, buttune the noise distribution to accommodate 5 more carriers, with aslight SNR penalty. For example, the Case II example scenario maysupport 16 carriers of 256QAM, and 16 carriers of 64QAM, whereas theCase IV example scenario may accommodate 5 additional carriers, but withonly 12 of the Case IV carriers having sufficient SNR to support 256QAM(i.e., the remaining 25 Case IV carriers will only support 64QAM.Nevertheless, in the Case IV example scenario, the overall LTE data rateis improved by approximately 10%.

For the exemplary embodiments described in Table 4, above, and also withrespect to the following embodiments, the exemplary carriers aredescribed as LTE carriers (e.g., Table 2), for purposes of illustration.Nevertheless, the person of ordinary skill in the art will understandthat these examples are provided for ease of explanation, and are notintended to be limiting. Thus, as shown in Table 2, CPRI consumes 1228.8Mb/s MFH capacity for each LTE carrier. In contrast, as shown in Table4, according to the present delta-sigma digitization interfacetechniques, each LTE carrier consumes 270.27-625 Mb/s MFH capacity, andthe resultant spectral efficiency (SE) is improved by 1.97-4.55 times incomparison with CPRI.

FIG. 12 is a flow diagram for a digitization process 1200. Similar toprocess 1100, FIGS. 11A-D, digitization process 1200 may also beexecuted by a processor of one or more BBUs for implementing the presentflexible delta-sigma digitization interface, and with respect tocarriers, such as LTE, for example, having particular data raterequirements.

In an exemplary embodiment, the number of LTE carriers and theirparticular modulation formats may be selected according to the demandedLTE data rate. SNR requirements and the number of quantization bits maythen be determined, while keeping the EVM performance of each LTEcarrier compatible with 3GPP specifications. According to the determinednoise distribution, zeros and poles of a noise transfer function (NTF)may then be calculated, and a Z-domain block diagram may be implementedfor the design of the delta-sigma ADC, based on the NTF and quantizationbit number.

In an embodiment, digitization process 1200 may be implemented as aseries of logical steps. The person of ordinary skill in the art though,will understand that except where indicated to the contrary, one or morethe following steps may be performed in a different order and/orsimultaneously. In the exemplary embodiment, process 1200 begins at step1202, in which the LTE data rate requirements are obtained. In step1204, process 1200 selects the number of LTE carriers according to theLTE data rate requirements obtained in step 1202. In an exemplaryembodiment of step 1204, the particular LTE data rate requirements arepreviously known, i.e., stored in a memory of, or in operablecommunication with, the respective processor implementing process 1200.In step 1206, process 1200 selects the LTE modulation format(s)applicable to the obtained data rate and the selected carriers.

In step 1208, process 1200 determines the SNR requirements according tothe relevant communication standard (3GPP, in this example), and inconsideration of the LTE carriers and modulation formats selected. Instep 1210, process 1200 may additionally obtain the particular EVMrequirements of the relevant standard (e.g., 3GPP), such that the EVMperformance of each LTE carrier may be maintained according to theparticular standard. Step 1210 may, for example, be performed before,after, or simultaneously with step 1208.

After the SNR requirements are determined, process may implementseparate sub-process branches. In an exemplary first branch/subprocess,in step 1212, process 1200 determines the quantization bit number. In anexemplary embodiment, step 1214 may be performed in an exemplary secondbranch/subprocess. In step 1214, process 1200 calculates the zeros andpoles for the NTF. In step 1216, process 1200 determines the NTF anddistribution of quantization noise in the frequency domain correspondingto the zeros and poles selected in step 1214. In step 1218, process 1200implements a logical Z-domain block filter configuration having an ordercorresponding to the number of zeros of the NTF. In step 1220, process1200 configures the delta-sigma ADC from the quantization bitsdetermined in step 1212 and from the Z-domain block configurationimplemented in step 1216.

FIG. 13A is a schematic illustration of a filter 1300. In an embodiment,filter 1300 may represent a Z-block diagram and/or impulse responsefilter for a delta-sigma ADC according to the systems and methodsdescribed herein. In an exemplary embodiment, filter 1300 is asecond-order delta-sigma ADC that may be implemented for the Case Iimplementation scenario illustrated in Table 4. More particularly, inthe example depicted in FIG. 13A, filter 1300 operates with respect to asecond-order delta-sigma ADC working at 10 GSa/s with one quantizationbit and, after digitization, filter 1300 operates to transform 32 LTEcarriers, at an input 1302, into a 10 Gb/s OOK signal, for example, atan output 1304. In an exemplary embodiment, because the relevant NTF ofthe delta-sigma ADC has an order of two, filter 1300 includes twofeedforward coefficients a, and two feedback loops 1306 each having az⁻¹ delay cell. In the embodiment depicted in FIG. 13A, filter 1300includes a “DAC” recursion 1308 for implementing the delta-sigma memoryeffect of past outputs, described above, and a one-bit quantizer 1310.

FIG. 13B is a graphical illustration depicting an I-Q plot 1312 for theNTF for filter 1300, FIG. 13A. Plot 1312 illustrates the respectivezeros and poles of the second-order NTF for filter 1300, which has aconjugate pair of zeros, and a conjugate pair of poles. In theembodiment depicted in FIG. 13B, the two conjugate zeros may be seen todegenerate to z=1, which corresponds to a DC frequency (i.e.,f=0).

FIG. 13C is a graphical illustration depicting a frequency response 1314of the NTF for filter 1300, FIG. 13A. In an exemplary embodiment,frequency response 1314 represents a distribution of quantization noisein the frequency domain. In an embodiment of the delta-sigma ADCdescribed herein, the distribution of quantization noise is uneven, andmay therefore be determined by the zeros of the NTF (e.g., FIG. 13B).That is, each zero corresponds to a null point 1316 of quantizationnoise on frequency response 1314. In this example, using a sampling rateof 10 GSa/s, the relevant Nyquist zone is shown to occur in the range of0-5 GHz. The only zero may then be seen to be located along frequencyresponse 1314 at f=0. Accordingly, the quantization noise is shown to beminimized at DC, and to rapidly increase with frequency along frequencyresponse 1314.

Thus, according to the embodiments depicted in FIGS. 13A-C, LTE carriersat lower frequencies may be seen to have smaller quantization noise andhigher SNR, while also supporting higher modulation formats. Incontrast, the higher frequency carriers are seen to have smaller SNR,and will only be capable of accommodating lower modulation formats. Theexemplary second-order configuration may therefore be capable ofaccommodating 32 LTE carriers with differential SNR provisioning, wherethe first 18 carriers thereof will have sufficient SNR to accommodate a64QAM modulation format, and the remaining 14 carriers will be capableof supporting a 16QAM modulation format.

According to the exemplary embodiment of FIGS. 13A-C, afterdigitization, 32 LTE carriers may be transformed into a 10 Gb/s digitalOOK signal. Accordingly, each individual LTE carrier will consume 312.5Mb/s MFH capacity (i.e., 10 Gb/s /32 carriers=312.5 Mb/s per carrier).Compared with CPRI, where each LTE carrier consumes a MFH capacity of1228.8 Mb/s, the spectral efficiency is improved by 3.93 times accordingto the present embodiments.

FIG. 14A is a graphical illustration depicting a spectrum plot 1400. Inan exemplary embodiment, spectrum plot 1400 illustrates the frequencyspectrum including the 32 LTE carriers digitized by a second-orderone-bit delta-sigma ADC (e.g., FIG. 13A). In the example depicted inFIG. 14A, the respective spectra of the 32 LTE carriers are containedwithin a carrier spectrum portion 1402. In some embodiments, to furtherimprove the SNR of LTE carriers at high frequencies, a pre-emphasis maybe used to boost the power of high frequency carriers.

FIG. 14B is a graphical illustration depicting a close-up view ofcarrier spectrum portion 1402, FIG. 14A. Within the close-up view, thefirst 18 of the 32 LTE carriers (i.e., at 64QAM) may be more readilydistinguished from the remaining 14 LTE carriers (i.e., at 16QAM).

FIG. 15A is a graphical illustration 1500 depicting the EVMs for the LTEcomponent carriers depicted in FIG. 14B. In the example depicted in FIG.15A, the first 18 component carriers (i.e., 64QAM) exhibit an EVMpercentage below 8%, and the remaining 14 component carriers (i.e.,16QAM) exhibit an EVM percentage above 8% and below 12.5%.

FIG. 15B is a graphical illustration of constellation plots 1502, 1504,1506, 1508 for best case and worst case scenarios for the carriersdepicted in FIG. 15A. More specifically, constellation plot 1502demonstrates the best case scenario for the 64QAM component carriers,which occurs at the first component carrier (i.e., CC1) exhibiting thelowest EVM percentage of the group (e.g., illustration 1500, FIG. 15A).Constellation plot 1504 demonstrates the worst case scenario for the64QAM component carriers, which occurs at the last of the 18 componentcarriers (i.e., CC18) exhibiting the highest EVM percentage of thegroup. Similarly, constellation plot 1506 demonstrates the best casescenario for the 16QAM component carriers, which occurs at the firstcomponent carrier of the 14-carrier group (i.e., CC19) exhibiting thelowest relative EVM percentage, and constellation plot 1508 demonstratesthe worst case scenario for the 16QAM component carriers, which occursat the last of the 16QAM component carriers (i.e., CC32) exhibiting thehighest EVM percentage.

From these constellations, it can be seen how the respectiveconstellation points are much more closely clustered in the respectivebest case scenarios (i.e., constellation plots 1502, 1506), but appearmore to exhibit more distortion in the respective worst case scenarios(i.e., constellation plots 1504, 1508). As can be further seen from theforegoing embodiments, innovative second-order delta-sigma ADCs may beadvantageously realized using only one- or two-feedback loops, whichprovide simple and low-cost implementation incentives. Accordingly, theperson of ordinary skill in the art will understand that systems andmethods according to the Case I implementation example are particularlysuitable for scenarios having relatively low SNR and low data raterequirements.

FIG. 16A is a schematic illustration of a filter 1600. In an embodiment,filter 1600 also represents a Z-block diagram and/or impulse responsefilter for a delta-sigma ADC according to the systems and methodsdescribed herein.

In an exemplary embodiment, filter 1600 constitutes fourth-orderdelta-sigma ADC for the Case II and Case III implementation scenariosillustrated in Table 4, above. More particularly, in the exampledepicted in FIG. 16A, filter 1600 operates similarly, in some respects,to filter 1300, FIG. 13A, but as a fourth-order system, in contrast tothe second-order system of FIG. 13A. That is, between an input 1602 andan output 1604, filter 1600 includes four feedforward coefficients a,and also four feedback loops 1606 each having a z⁻¹ delay cell,corresponding to the order of 4. In the embodiment depicted in FIG. 16A,filter 1600 further includes two feedback coefficients g, a DACrecursion 1608 for implementing the delta-sigma memory effect, and aquantizer 1610.

In some embodiments, the same general filter architecture of filter 1600may be implemented for both of the Case II and Case III examplescenarios, except that, in Case II, quantizer 1610 is a one-bitquantizer that outputs only two levels, similar to quantizer 1310, FIG.13A (i.e., Case I). In Case III though, quantizer 1310′ is a two-bitquantizer that outputs four levels.

FIG. 16B is a graphical illustration depicting an I-Q plot 1612 for anNTF for filter 1600, FIG. 16A. Plot 1612 illustrates the respectivezeros and poles of the fourth-order NTF for filter 1600, which, incontrast to plot 1312, FIG. 13B, has two conjugate pairs of zeros, andtwo conjugate pairs of poles.

FIG. 16C is a graphical illustration depicting a frequency response 1614of the NTF for filter 1600, FIG. 16A. In an exemplary embodiment,similar to frequency response 1314, FIG. 13C, frequency response 1614represents a distribution of quantization noise in the frequency domain.Different though, from frequency response 1314, frequency response 1614includes two null points 1616.

FIG. 17A is a graphical illustration depicting a spectrum plot 1700. Inan exemplary embodiment, spectrum plot 1700 illustrates a frequencyspectrum including the 32 LTE carriers digitized by a fourth-orderone-bit delta-sigma ADC (e.g., FIG. 16A, Case II). In the exampledepicted in FIG. 17A, the respective spectra of the 32 LTE carriers arecontained within a carrier spectrum portion 1702.

FIG. 17B is a graphical illustration depicting a close-up view ofcarrier spectrum portion 1702, FIG. 17A. Similar to the Case Iimplementation scenario (e.g., FIG. 14B), within this close-up view, itcan be seen that this design configuration will also support 32 LTEcarriers. However, due to the increased order of delta-sigma ADC (i.e.,from second to fourth), the in-band quantization noise in this Case IIscenario is significantly reduced in comparison with Case I, and ahigher SNR and modulation may therefore be provisioned. In this Case IIexample, all 32 LTE carriers may be seen to have sufficient SNR tosupport a 64QAM modulation format, and half of the carriers (i.e., 16)have sufficient SNR to support a 256QAM modulation format. The RFspectrum and EVMs of all 32 carriers in the Case II scenario aredescribed further below with respect to FIG. 18.

FIG. 18A is a graphical illustration 1800 depicting the EVMs for the 32Case II LTE component carriers depicted in FIG. 17B. That is,illustration 1800 depicts the EVM percentages of 32 carriers digitizedby a fourth-order one-bit delta-sigma ADC. In the example depicted inFIG. 18A, 16 of the 32 component carriers (i.e., 256QAM) exhibit an EVMpercentage below 3.5%, and the remaining 16 carriers (i.e., 64QAM)exhibit an EVM percentage above 3.5% and below 8%.

FIG. 18B is a graphical illustration of constellation plots 1802, 1804,1806, 1808 for best case and worst case scenarios for the carriersdepicted in FIG. 18A. More specifically, constellation plot 1802demonstrates the best case scenario for the 256QAM component carriers,which occurs at the twelfth component carrier (i.e., CC12) exhibitingthe lowest EVM percentage of the modulation format group. Constellationplot 1804 thus demonstrates the worst case scenario for the 256QAMcomponent carriers, which occurs at the seventeenth component carrier(i.e., CC17), in this example. Similarly, constellation plot 1806demonstrates the best case scenario for the 64QAM component carriers,which occurs at the sixth component carrier (i.e., CC6), andconstellation plot 1808 demonstrates the worst case scenario for the64QAM component carriers, which occurs at the thirty-second componentcarrier (i.e., CC32).

Fourth-order delta-sigma ADC techniques are more complex thansecond-order ADC techniques. However, fourth-order delta-sigma ADCcomparatively enables significantly reduced in-band quantization noiseand enhanced SNR. The present fourth-order delta-sigma ADC embodimentsare of particular use for high SNR and data rate scenarios, and canpotentially support more LTE carriers. In this exemplary implementationscenario, 32 LTE carriers are shown to be supported. As describedfurther below with respect to the Case IV and V implementationscenarios, the present fourth-order delta-sigma ADC embodiments may alsosupport up to 37 LTE carriers as well.

FIG. 19A is a graphical illustration depicting a spectrum plot 1900. Inan exemplary embodiment, spectrum plot 1900 illustrates a frequencyspectrum including the 32 LTE carriers digitized by a fourth-ordertwo-bit delta-sigma ADC (e.g., FIG. 16A, Case III). In the exampledepicted in FIG. 19A, the respective spectra of the 32 LTE carriers arecontained within a carrier spectrum portion 1902. As described above,the Case III implementation scenario uses the same fourth-orderdelta-sigma ADC as in Case II, except for a two-bit quantizer (e.g.,quantizer 1310′, FIG. 13A) instead of a one-bit quantizer (e.g.,quantizer 1310). Accordingly, both of the Case II and Case III scenariosshare the same zeroes and poles (e.g., FIG. 16B), as well as the sameNTF frequency distribution (e.g., FIG. 16C). In Case III though, thetwo-bit quantizer is configured to output a PAM4 signal. The presence ofthis additional quantization bit enables the present embodiments,according to this example, to realize further reductions in thequantization noise, while also achieving higher SNR provisioning.

FIG. 19B is a graphical illustration depicting a close-up view ofcarrier spectrum portion 1902, FIG. 19A. Similar to the Case IIimplementation scenario (e.g., FIG. 17B), within this close-up view, itcan be seen that the design configuration for this Case III scenariowill also support 32 LTE carriers. In the Case III scenario though, dueto the additional quantization bit, the total MFH capacity is increasedto 20 Gb/s. Additionally, in this implementation scenario, the fronthaulcapacity consumed by each LTE carrier is also doubled in comparison withthe respective capacities of the Case I and Case II implementationscenarios. This Case III implementation scenario is thereforeparticularly useful and instances where it is desirable to tradespectral efficiency for SNR. Nevertheless, as can be seen in Table 4,the spectral efficiency under the Case III implementation scenario isstill 1.97 times greater than CPRI. The RF spectrum and EVMs of the 32Case III carriers are described further below with respect to FIG. 20.

FIG. 20A is a graphical illustration 2000 depicting the EVMs for the 32Case III LTE component carriers depicted in FIG. 19B. That is,illustration 2000 depicts the EVM percentages of 32 carriers digitizedby a fourth-order two-bit delta-sigma ADC. In the example depicted inFIG. 20A, all 32 component carriers have sufficient SNR to support256QAM , i.e., all 32 carriers exhibit an EVM percentage below 3.5%.Furthermore, because 10 of the component carriers exhibit an EVMpercentage below 1%, these 10 carriers will support 1024QAM.

FIG. 20B is a graphical illustration of constellation plots 2002, 2004,2006, 2008 for best case and worst case scenarios for the carriersdepicted in FIG. 20A. More specifically, constellation plot 2002demonstrates the best case scenario for the 1024QAM component carriers,which occurs at the twelfth component carrier (i.e., CC12).Constellation plot 2004 thus demonstrates the worst case scenario forthe 1024QAM component carriers, which occurs at the twenty-eighthcomponent carrier (i.e., CC28), in this example. Similarly,constellation plot 2006 demonstrates the best case scenario for the256QAM component carriers, which occurs at the sixteenth componentcarrier (i.e., CC16), and constellation plot 2008 demonstrates the worstcase scenario for the 256QAM component carriers, which occurs at thetwenty-second component carrier (i.e., CC22).

FIG. 21A is a graphical illustration depicting an I-Q plot 2100 for anNTF. In an exemplary embodiment, plot 2100 illustrates the respectivezeros and poles of a fourth-order NTF, for the Case IV implementationscenario, of a filter, such as filter 1600, FIG. 16A. Indeed, for easeof illustration, the Case IV and Case V scenarios may utilize the samerespective fourth-order delta-sigma ADC and Z-domain block diagramimplemented with respect to the Case II and Case III scenarios (e.g.,filter 1600, FIG. 16A). However, in the Case IV and Case Vimplementation scenarios, the coefficients on the feedback (i.e., g1,g2) and feedforward (i.e., a1, a2, a3, a4) paths may be differentlytuned to accommodate additional LTE carriers. In some embodiments, therespective two conjugate pairs of zeros in the Case IV scenario may bemore separated from each other than in the Case II scenario (e.g., FIG.16B).

FIG. 21B is a graphical illustration depicting a frequency response 2102of the NTF for I-Q plot 2100, FIG. 21A. In an exemplary embodiment,frequency response 2102 is similar to frequency response 1614, FIG. 16C,and includes two null points 2104. In some embodiments, where therespective conjugate pairs of zeros exhibit more separation from eachother, null points 2104 may similarly exhibit greater separation fromone another in relation to the Case II scenario (e.g., FIG. 16C). TheCase IV implementation scenario it is therefore particularlyadvantageous where it is desirable to accommodate as many LTE carriersas possible with maximized spectral efficiency.

In comparison with the Case II implementation scenario, the Case IVimplementation scenario supports 37 LTE carriers with slight SNRpenalty. Additionally, the MFH capacity consumed per carrier in thisCase IV scenario is reduced to 270.27 Mb/s, and the spectral efficiencyis improved by 4.55 times in comparison with CPRI.

FIG. 22A is a graphical illustration depicting a spectrum plot 2200. Inan exemplary embodiment, spectrum plot 2200 illustrates a frequencyspectrum including 37 LTE carriers digitized by a fourth-order one-bitdelta-sigma ADC (e.g., FIG. 16A, Case III). In the example depicted inFIG. 22A, the respective spectra of the 37 LTE carriers are containedwithin a carrier spectrum portion 2202. In this Case IV implementationscenario, the same one-bit quantizer (e.g., quantizer 1310, FIG. 13A)may be used in the fourth order delta-sigma ADC as was used in the CaseII scenario.

FIG. 22B is a graphical illustration depicting a close-up view ofcarrier spectrum portion 2202, FIG. 22A. Different from the Case IIimplementation scenario (e.g., FIG. 17B), within this close-up view, itcan be seen that the design configuration for this Case IV scenario willsupport 37 LTE carriers. The RF spectrum and EVMs of the 37 Case IVcarriers are described further below with respect to FIG. 23.

FIG. 23A is a graphical illustration 2300 depicting the EVMs for the 37Case IV LTE component carriers depicted in FIG. 22B. That is,illustration 2300 depicts the EVM percentages of 37 carriers digitizedby a fourth-order one-bit delta-sigma ADC. In the example depicted inFIG. 23A, all 37 component carriers have sufficient SNR to support a64QAM modulation format, i.e., all 37 carriers exhibit an EVM percentagebelow 8%. Additionally, 12 of the Case IV component carriers exhibit anEVM percentage below 3.5%, and may therefore support a 256QAM modulationformat.

FIG. 23B is a graphical illustration of constellation plots 2302, 2304,2306, 2308 for best case and worst case scenarios for the carriersdepicted in FIG. 23A. More specifically, constellation plot 2302demonstrates the best case scenario for the 64QAM component carriers,which occurs at the tenth component carrier (i.e., CC10). Constellationplot 2304 demonstrates the worst case scenario for the 64QAM componentcarriers, which occurs at the thirty-seventh component carrier (i.e.,CC37), in this example. Similarly, constellation plot 2306 demonstratesthe best case scenario for the 256QAM component carriers, which occursat the fourteenth component carrier (i.e., CC14), and constellation plot2308 demonstrates the worst case scenario for the 256QAM componentcarriers, which occurs at the thirty-second component carrier (i.e.,CC32).

FIG. 24A is a graphical illustration depicting a spectrum plot 2400. Inan exemplary embodiment, spectrum plot 2400 illustrates a frequencyspectrum including 37 LTE carriers digitized by a fourth-order two-bitdelta-sigma ADC (e.g., FIG. 16A, Case III). In the example depicted inFIG. 24A, the respective spectra of the 37 LTE carriers are containedwithin a carrier spectrum portion 2402. In this Case V implementationscenario, the same two-bit quantizer (e.g., quantizer 1310′, FIG. 13A)may be used in the fourth order delta-sigma ADC as was used in the CaseIII scenario. In other words, the Case V implementation scenario issimilar to the Case IV implementation scenario, except that in Case V,the one-bit Case IV quantizer is replaced with a two-bit quantizer. Thezeros, poles, and frequency response of the corresponding NTF though,remain the same as with the Case IV scenario.

Due to the increase from one quantization bit to two quantization bits,the quantization noise in the Case V scenario is reduced in comparisonwith the Case IV scenario. Furthermore, in the Case V scenario, all 37LTE carriers have sufficient SNR to support a 256QAM modulation format,and 8 of the 37 carriers exhibit an EVM less than 1%, and may thereforesupport up to a 1024QAM modulation format.

FIG. 24B is a graphical illustration depicting a close-up view ofcarrier spectrum portion 2402, FIG. 24A. Different from the Case IIIimplementation scenario (e.g., FIG. 19B), within this close-up view, itcan be seen that the design configuration for this Case V scenario willsupport 37 LTE carriers. The RF spectrum and EVMs of the 37 Case Vcarriers are described further below with respect to FIG. 25.

FIG. 25A is a graphical illustration depicting the EVMs for the 37 CaseV LTE component carriers depicted in FIG. 24B. That is, illustration2500 depicts the EVM percentages of 37 carriers digitized by afourth-order two-bit delta-sigma ADC. In the example depicted in FIG.25A, all 37 component carriers have sufficient SNR to support a 256QAMmodulation format, i.e., all 37 carriers exhibit an EVM percentage below3.5%. Additionally, eight of the Case V component carriers exhibit anEVM percentage below 1%, and may therefore support a 1024QAM modulationformat.

FIG. 25B is a graphical illustration of constellation plots 2502, 2504,2506, 2508 for best case and worst case scenarios for the carriersdepicted in FIG. 25A. More specifically, constellation plot 2502demonstrates the best case scenario for the 256QAM component carriers,which occurs at the thirty-third component carrier (i.e., CC33).Constellation plot 2504 demonstrates the worst case scenario for the256QAM component carriers, which occurs at the thirty-seventh componentcarrier (i.e., CC37), in this example. Similarly, constellation plot2506 demonstrates the best case scenario for the 1024QAM componentcarriers, which occurs at the twelfth component carrier (i.e., CC12),and constellation plot 2508 demonstrates the worst case scenario for the1024QAM component carriers, which occurs at the fifteenth componentcarrier (i.e., CC15).

According to the systems and methods described herein, an innovativeflexible digitization interface is provided that is based on delta-sigmaADC, and which enables on-demand SNR and LTE data rate provisioning innext generation MFH networks. The present embodiments advantageouslyeliminate the need for conventional DAC at the RRH by providing asimplified architecture that allows replacement with a DAC by a BPF,which significantly reduces the cost and complexity of small celldeployment.

According to the techniques described herein, a simplified, DAC-free,all-analog implementation of RRHs it may also be effectively provided.These all-analog RRH implementations offer additional flexibility to thedigitization interface in terms of sampling rate, quantization bits, andquantization noise distribution. Through exploitation of the noiseshaping techniques described herein, the present systems and methods arefurther capable of manipulating the frequency distribution ofquantization noise as needed or desired. By allowing for a more flexiblechoice of sampling rate, quantization bits, and noise distribution, thepresent systems and methods significantly improve over conventionalsystems by enabling an efficient capability for on-demand SNR and datarate provisioning. In comparison with conventional CPRI, the presentdigitization interface embodiments are capable of improving the spectralefficiency by at least 1.97-4.55 times.

Real-Time Implementation

Proof of the concepts of the present systems and methods is demonstratedwith respect to several real-time implementations. In one exemplaryimplementation, delta-sigma ADC as demonstrated using a real-timefield-programmable gate array (FPGA). The FPGA-based system provides a5-GSa/s delta-sigma ADC capable of digitizing signals up to 252 MHz 5G(LTE, in this example, backspace), using a 1024QAM modulation format andhaving an EVM less than 1.25%. Additionally, the following embodimentsfurther provide an innovative digitization approach that enables greaterfunctional split options for next generation fronthaul interfaces(NGFIs).

As described above, an improved delta-sigma ADC is provided thatdelivers bandwidth efficiency four times better than conventional CPRItechniques. For ease of explanation, some of the exemplary embodimentsabove are described with respect to a low-pass ADC that may be emulatedby offline processing (e.g., a waveform generator). As described furtherabove, in such cases, RF up-conversion would still be necessary at eachRRU.

In further exemplary embodiments, an NGFI according to the presentsystems and methods is configured to implement a real-time FPGA-basedbandpass delta-sigma ADC. This real-time bandpass delta-sigma ADC bothfurther improves the bandwidth efficiency, and also enables digitizationof mobile signals “AS IS” at respective radio frequencies withoutrequiring frequency conversion. This additional functionality furthersimplifies the RRU design in a significant manner by eliminating theconventional need for a local oscillator and RF mixer. Thesearchitectural improvements may be implemented singly, or in combinationwith one or more of the innovative configurations described above.

The present systems and methods further enable an innovative functionalsplit option for NGFIs. In an exemplary embodiment, a significantportion of RF functionality is consolidated in a distributed unit (DU),which enables a significantly simplified, and thus lower-cost,configuration at the RRU for small cell deployment. In an exemplaryimplementation, a high-performance FPGA (e.g., XILINX VC707) is employedas a bandpass delta-sigma ADC, using a 5 GSa/s sampling rate and havinga widest reported signal bandwidth of 252 MHz. In such exemplaryconfigurations, real-time digitization may be provided for both 5G-newradio (5GNR) and LTE signals, and for modulation formats up to 1024QAMhaving an EVM less than 1.25%.

FIG. 26 is a graphical illustration of a comparative summary plot 2600of delta-sigma RF sampling rates taken against conversion bandwidths.More specifically, comparative summary plot 2600 graphically illustratesknown, reported results 2602 of a plurality of delta-sigma ADC studiesthat have been performed by numerous universities, corporations, andresearch centers. It can be seen, from reported results 2602 that all ofthese recent studies, with one exception (i.e., reported result 2602(4), MIT) are all confined to bandwidths of between zero and 50 MHz,irrespective of the noted sampling rate.

Reported result 2602 (4) is the lone exception to this trend, indicatinga 200 MHz bandwidth increase at a sampling rate between 2 and 3 GHz.However, reported result 2602 (4) does not rise above a 3 GHz samplingrate. In contrast, according to the present systems and methods, a setof present results 2604, namely, that of the real-time implementationsdescribed herein, are illustrated to all locate at an approximately 5GHz sampling rate, and all for bandwidths ranging between 100 MHz at thelow end, to 250 MHz at the high-end. Accordingly, the present systemsand methods are configured to operate at considerably higher samplingrates (e.g., 5 GSa/s) and bandwidths (e.g., up to 100-250 MHz andgreater) than all of the known, reported delta-sigma ADCimplementations. FIG. 26 is just one example of the superior qualitiesprovided according to the present techniques.

FIG. 27 is a schematic illustration of a network architecture 2700. Inan exemplary embodiment, architecture 2700 is similar in some respectsto architecture 100, FIG. 1, and may represent a C-RAN architectureincluding an MBH network portion 2702, a first MFH network portion 2704,and a second MFH network portion 2706. In the exemplary embodiment,architecture 2700 further includes a core network 2708 and a pluralityof S-GW/MMEs 2710 in communication with a central unit (CU) 2712 inoperable communication with MBH network portion 2702. That is, MBHnetwork portion 2702 constitutes the network segment from S-GW/MMEs 2710and core network 2708 to CU 2712.

Architecture 2700 further includes one or more RRHs 2714 (also referredto as remote radio units, or RRUs), accessible by mobile and/or wirelessusers (not separately shown in FIG. 27). A plurality of DUs 2716 are inoperable communication with CU 2712, and serve to facilitatecommunication between CU 2712 and one or more RRHs 2714. In someembodiments, each DU 2716 may include one or more BBUs or a BBU pool(not separately shown). In at least one embodiment, CU 2712 may includeadditional BBUs or BBU pools. Accordingly, first MFH network portion2704 constitutes the network segment from DUs 2716 to RRHs 2714, andsecond MFH network portion 2704 constitutes the combination of CU 2712and DUs 2716.

In exemplary operation of architecture 2700, general functionality maybe similar to that of architecture 100, FIG. 1. Different fromarchitecture 100 though, in architecture 2700, NGFI functions are splitand/or shared between CU 2712, DU 2716, and RRHs 2714. An NGFIfunctional layer diagram 2718 illustrates exemplary NGFI functionalsplit options between several functional layers of CU 2712, DU 2716, andRRH 2714, which options are schematically represented in diagram 2718 asnumbered connections between the various layers. For example, CU 2716includes a radio resource control (RRC) layer 2720 and a packet dataconvergence protocol (PDCP) layer 2722, with a split option 1 indicatedtherebetween. Additionally, in this example, DU 2716 includes one ormore of a high radio link control (RLC) layer 2724, a low RLC layer2726, a high media access control (MAC) layer 2728, a low MAC controllayer 2730, a high physical (PHY) layer 2732, a low PHY layer 2734, anda high RF layer 2736. RRH 2714 includes a low RF layer 2738 in operablecommunication with high RF layer 2736 of DU 2716, through split option9.

During the evolution to 5G, NGFI was proposed to split basebandfunctions into a central unit and a distributed unit, thereby dividing aC-RAN architecture (e.g., architecture 2700) into three segments: (1) anMBH segment (e.g., MBH network portion 2702) from service gateways(e.g., S-GW 2710) to the BBU; (2) one fronthaul segment (e.g., secondMFH network portion 2706) from the CU (e.g., CU 2712) to the DU (e.g.,DU 2716); and (3) another fronthaul segment (e.g., first MFH networkportion 2704) from the DU to the RRU (e.g., RRH 2714). Some of the splitoptions depicted in diagram 2718 became achievable according to thisoriginal NGFI proposal. However, using the architectural and functionalimprovements of the embodiments herein, the present bandpass delta-sigmaADC techniques newly enable split option 9 (i.e., between high-RF layer2736 and low-RF layers 2738) as being achievable due to theconsolidation of a significant portion of the RF functions in the DU.This consolidation at the DU advantageously lowers both the cost andcomplexity of the RRU architecture and functionality which therebyfacilitates a substantially denser deployment of small cells.

FIG. 28 is a schematic illustration of an RoF link 2800. RoF link 2800includes at least one DU 2802 in operable communication with at leastone RRU 2804 over a transport medium 2806 (e.g., a single mode fiber, orSMF). In an exemplary embodiment, DU 2802 includes one or more fronthaultechnologies of an analog link portion 2808, a first digital linkportion 2810, and a second digital link portion 2812. Analog linkportion 2808, for example, serves to provide RoF-based analog MFHfunctionality, similar to MFH network 200, FIG. 2A. Similarly, firstdigital link portion 2810 serves to provide CPRI-based digital MFHfunctionality, similar to MFH network 300, FIG. 3A, and second digitallink portion 2812 serves to provide bandpass delta-sigma ADC-baseddigital MFH functionality, similar to MFH network 400, FIG. 4A.

More specifically, analog link portion 2808 includes, at DU 2802, abaseband processing layer 2814, an RF up-conversion layer 2816, an FDM2818, and an E/O interface 2820, and at RRU 2804, a complementary RFfront end 2822, a first power amplifier 2824, a first BPF 2826, and anO/E interface 2828. Similarly, first digital link portion 2810 includes,at DU 2802, a baseband processing layer 2830, a compression unit 2832, aNyquist ADC 2834, a first TDM 2836, and an E/O interface 2838, and atRRU 2804, a complementary RF front end 2840, a second power amplifier2842, an RF up-converter 2844, a decompression unit 2846, a Nyquist DAC2848, a second TDM 2850, and an O/E interface 2852. Additionally, seconddigital link portion 2812 includes, at DU 2802, a baseband processor2854, an RF up-converter 2856, a delta-sigma ADC 2858 (e.g., a bandpassdelta-sigma ADC), and an E/O interface 2860, and at RRU 2804, acomplementary RF front end 2862, a second BPF 2864, a third poweramplifier 2866, and an O/E interface 2868.

According to the exemplary configuration of link 2800, a simplified,inexpensive system is obtained, which provides high spectral efficiency.Limitations due to nonlinear impairments are also advantageouslyaddressed by the innovative configuration therein. For example, theCPRI-based digital MFH system of first digital link portion 2810implements Nyquist ADC at DU 2802, and DAC at RRU 2804, todigitize/retrieve the analog waveforms of baseband signals.Nevertheless, RF up-conversion performance is still necessary at RRU2804. Because CPRI-based solutions only work at fixed chip rates (e.g.,3.84 MHz), synchronization presents a significant challenge fordifferent radio access technologies such as LTE, 5G, Wi-Fi, etc.However, by implementing the innovative functional split provided bysplit option 9 (e.g., FIG. 27) at second digital link portion 2812 ofthe same DU (e.g., DU 2802), the limitations of the CPRI-based digitalMFH system may be avoided, or at least significantly mitigated.

More particularly, at DU 2802, mobile signals may be up-converted toradio frequencies and digitized “AS IS” by bandpass delta-sigma ADC2858. Additionally, at RRU 2804, a conventional DAC is replaced by thelower-cost second BPF 2864 to retrieve the analog waveform. As describedabove, the retrieved analog waveform is then ready for wirelesstransmission without the need for RF up-conversion. The operationalprinciples of bandpass delta-sigma ADC 2858 and second BPF 2864 aredescribed above in greater detail with respect to FIGS. 6 and 7, and theoperational principles of Nyquist ADC 2834 are described in greaterdetail with respect to FIG. 5. That is, in summary, delta-sigma ADCtechniques are different from Nyquist ADC in that delta-sigma ADC tradesquantization bit(s) for the sampling rate.

For example, as described above, delta-sigma ADC enables use of a highsampling rate with only one quantization bit (or two bits). The inputsignal is first oversampled, followed by exploitation of a noise shapingtechnique to push the quantization noise out of the signal band, so thatthe signal and noise are separated in the frequency domain. Using theseinnovative techniques at delta-sigma ADC 2858, the analog waveform maybe easily retrieved at RRU 2804 by second BPF 2864, which filters outthe OOB noise.

In the exemplary embodiment, in analog link portion 2808, first poweramplifier 2824 is deployed after first BPF 2826 to amplify the analogsignals, whereas in second digital link portion 2812, third poweramplifier 2866 is deployed before second BPF 2864 to boost the OOKsignal (or a PAM4 signal, in the case where two quantization bits areused). Link 2800 is thus able to advantageously avoid the amplifiernonlinearity limitations described above, and further provide for use ofa significantly lower-cost, higher-efficiency, switch-mode poweramplifier than would be realized according to conventional techniques.

FIG. 29 is a schematic illustration of a system architecture 2900.System architecture 2900 represents a real-time experimentalimplementation of the architectures in operating principles describedherein. In the exemplary embodiment, system architecture 2900 representsa three-stage implementation, including an analog input source 2902, anFPGA 2904, and a fronthaul system 2906. In the real-time implementationof architecture 2900, FPGA 2904 receives the analog signal of analoginput source 2902 using an ADC interface 2908. In this implementation,ADC interface 2908 was a 4DSP FPGA Mezzanine Card (FMC170) inserted onthe high-pin count (HPC) connector of a Xilinx VC707 FPGA of FPGA 2904,which realizes a 5 GSa/s one-bit bandpass delta-sigma ADC as ADCinterface 2908 of the input analog signal. The person of ordinary skillin the art though, will understand that these specific hardwarecomponents are described for illustrative purposes, and are not intendedto be limiting. Other structural components may be utilized withoutdeparting from the scope of the principles described herein.

In exemplary operation, ADC interface 2908 samples the input analogsignal from input source 2902 at 5 GSa/s, with 10 bits per sample. FPGA20904 then performed one-bit delta-sigma modulation to transform 10input bits, at an input buffer 2910, into one output bit at an outputbuffer 2912. FPGA 2904 was then configured to output the resulting oneoutput bit through a multi-gigabit transceiver (MGT) port 2914. In thisexemplary configuration, due to the speed limitations of FPGA 2904, theFPGA configuration was pipelined to de-serialize the input data into 32pipelines, such that the operation speed of each pipeline wasindividually reduced to 156.25 MSa/s.

Fronthaul system 2906 thus represents a real-time experimental setupimplementation of a functional DU 2916 that includes FPGA 2904, and isin operable communication with a functional RRU 2918 over a 30 km SMFtransport medium 2920. In operation, DU 2916 generated real-time LTE and5G signals using a Rohde Schwarz (R&S) vector signal generator 2922 andan arbitrary waveform generator (AWG), respectively. FPGA 2904 then, forthis implementation, digitized the mobile signal(s) into a 5-Gb/s OOKsignal, which was then transmitted from DU 2916 to RRU 2918 over medium2920 using an optical IM-DD system. The real-time LTE signals werereceived at RRU 2918 by a BPF 2926, followed by an R&S signal analyzer2928. For the 5G signals, the received OOK signal was captured by a datastorage oscilloscope (DSO) 2930 followed by real-time DSP 2932. Therespective OFDM parameters of the several 5G/LTE signals of thisreal-time implementation are shown below in Table 5.

TABLE 5 Sampling Subcarrier Actual R-T rate FFT spacing Data Carrier BWModulation Case Signals (MSa/s) size (kHz) subcarriers number (MHz)(QAM) A 5G-NR 122.88 4096 30 3300 1 99 1024 B 2 198 256 × 2 C 4G- 30.722048 15 1200 10 180 256 × 6, LTE 1024 × 4 D 14 252 1024 × 2, 256 × 4, 64× 8

For Table 5, the 30 kHz subcarrier spacing and 3300 active subcarriersvalues for the 5G-NR signals are according to 3GPP Release 14. The EVMresults, as described above, may then be used to evaluate theperformance of the digitization. As described further below with respectto FIGS. 30-34, the EVM criteria used in accordance with 3GPP, similarto the embodiments described above, were: 12.5% EVM for the 16QAMmodulation format, 8% EVM for the 64QAM modulation format, and 3.5% EVMfor the 256QAM modulation format. Different from the embodiments abovethough, an EVM of 2% was used for the 1024QAM modulation format. Again,EVM for the 1024QAM modulation format is not yet specified for 3GPP. Theperson of ordinary skill in the art though, will understand that theoperating principles of the present embodiments fully apply to eitherEVM value for the 1024QAM modulation format.

FIG. 30A is a graphical illustration depicting a power spectral densityplot 3000 for an exemplary carrier. More particularly, power spectraldensity plot 3000 represents experimental results for Case A, Table 5,above, in which a single 960 MHz 5G carrier, having 99 MHz bandwidth andusing the 1024QAM modulation format, was digitized. Power spectraldensity plot 3000 illustrates the respective RF spectra of an inputanalog signal 3002 (e.g., 5G), an OOK signal 3004 after delta-sigma ADC,and a retrieved analog signal 3006 after BPF. In this experimentalimplementation, a 5-Gb/s error-free transmission was achieved over 30 kmfiber. It can also be seen from power spectral density plot 3000 thatretrieved analog signal 3006 tracks fairly closely with input analogsignal 3002 across the entire frequency range.

FIG. 30B is a graphical illustration of depicting a plot 3008 of EVM (in%) against received optical power for the carrier depicted in FIG. 30A.More particularly, plot 3008 illustrates EVM as a function of receivedoptical power, and with respect to several hardware simulations, such asfloating point, fixed point, and pipeline, which further illustrate theadvantageous step-by-step implementation of the present FPGAembodiments. As can be seen from plot 3008, no EVM penalty is observedafter 30-km fiber transmission.

FIG. 30C is a graphical illustration depicting a post-transmissionconstellation plot 3010 for the carrier depicted in FIG. 30A. Moreparticularly, constellation plot 3010 further confirms the integrity ofthe carrier transmission over a 30 km SMF.

FIG. 31A is a graphical illustration depicting a power spectral densityplot 3100 for an exemplary pair of carriers. More particularly, powerspectral density plot 3100 is similar to power spectral density plot3000, FIG. 30A, but represents experimental results for Case B of Table5, above, for a digitization implementation of two 5G carriers having atotal 198 MHz bandwidth and using the 256QAM modulation format. Powerspectral density plot 3100 illustrates the respective RF spectra ofinput analog signals 3102 (e.g., 5G), digitized OOK signal 3104, andretrieved analog signals 3106. In this experimental implementation, itcan be seen that, after transmission over 30 km fiber, quantizationnoise increases due to the wider signal bandwidth, and the EVMs of bothcarriers increases to 2.71% (see FIG. 31B, below) in comparison with thesingle-carrier case depicted in FIG. 30A. Nevertheless, as depicted inFIG. 31A, the results still satisfy the 3.5% EVM requirements of 3GPPfor the 256 QAM modulation format. It can also be seen from powerspectral density plot 3100 that retrieved analog signals 3106 track moreclosely with input analog signal 3102 at higher frequencies than atlower frequencies.

FIG. 31B is a graphical illustration depicting a plot 3108 of EVM (in %)against received optical power for the pair of carriers depicted in FIG.31A. More particularly, plot 3108 illustrates EVM as a function ofreceived optical power for both carriers, and with respect to theseveral hardware simulations depicted in plot 3008, FIG. 30B. Incomparison with plot 3008, plot 3108 demonstrates significant increasesfor each hardware simulation, in addition to the EVM increase describedabove.

FIGS. 32A-B are graphical illustrations depicting post-transmissionconstellation plots 3200, 3202 for the carriers depicted in FIG. 31A.More particularly, constellation plot 3200 illustrates thepost-transmission signal of the first carrier after 30 km, which has anEVM of 2.80%, and constellation plot 3202 illustrates thepost-transmission signal of the second carrier after 30 km, which has anEVM of 2.83%. As can be seen from constellation plots 3200, 3202, therelative signal integrity between the two carriers is substantiallysimilar, and within 3GPP requirements.

FIG. 33A is a graphical illustration depicting a power spectral densityplot 3300 for an exemplary set of carriers. More particularly, powerspectral density plot 3300 is similar to power spectral density plot3100, FIG. 31A, but represents experimental results for Case C of Table5, above, for a real-time digitization implementation of 10 LTE carriershaving a total 180 MHz bandwidth and where 6 of the 10 LTE carriers usedthe 256QAM modulation format, and the remaining 4 LTE carriers used the1024QAM modulation format. Power spectral density plot 3300 illustratesthe respective RF spectra of input analog signals 3302 (e.g., LTE),digitized signal 3304, and retrieved analog signals 3306. It can be seenfrom power spectral density plot 3300 that retrieved analog signals 3306track with input analog signals 3302 across most of the frequency rangeother than zero (i.e., DC).

FIG. 33B is a graphical illustration depicting a plot 3308 of EVM (in %)according to the respective carrier number of the set of 10 carriersdepicted in FIG. 33A. From plot 3308, it can be seen that the differentmodulations that are assigned to the respective carriers track fairlyclosely with one another across several different hardware simulations,but with the most significant deviation being between the direct 30 kmtransmission simulation and the FPGA hardware simulation.

FIG. 34A is a graphical illustration depicting a power spectral densityplot 3400 for an alternative set of carriers. More particularly, powerspectral density plot 3400 is similar to power spectral density plot3300, FIG. 33A, but represents experimental results for Case D of Table5, above, which represents a real-time digitization implementation of 14LTE carriers having a total 252 MHz bandwidth and where 8 of the 14 LTEcarriers used the 64QAM modulation format, 4 of the 14 LTE carriers usedthe 256QAM modulation format, and the remaining 2 LTE carriers used the1024QAM modulation format. Power spectral density plot 3400 illustratesthe respective RF spectra of input analog signals 3402 (e.g., LTE),digitized signal 3404, and retrieved analog signals 3406. It can be seenfrom power spectral density plot 3300 that retrieved analog signals 3406track more closely with input analog signals 3402 at lower frequenciesthan at higher frequencies, but still within desired results.

FIG. 34B is a graphical illustration depicting a plot 3408 of EVM (in %)according to the respective carrier number of the set of 14 carriersdepicted in FIG. 34A. From plot 3408, it can be seen that the differentmodulations that are assigned to the respective carriers track moreclosely with one another across the several different hardwaresimulations, then in the 10-carrier case illustrated in FIG. 33B. Thelargest still occurs between the direct 30 km transmission simulationand the FPGA hardware simulation, but this deviation is smaller than inthe 10-carrier case.

According to the embodiments described herein, innovative real-time,FPGA-based, bandpass delta-sigma ADC his advantageously implemented atthe 5 GSa/s sampling rate, and significantly beyond the widest reportedsignal bandwidth (e.g., FIG. 26) for the digitization of 5G and LTEsignals. According to the present embodiments, the bandwidth efficiencyof the fronthaul segment to the RRH is significantly improved, while thecost and complexity of the RRUs are significantly reduced. The presenttechniques therefore unable a new and useful functional split option forNGFI that significantly improves over conventional proposals.

Pipeline Implementation

In accordance with one or more of the systems and methods describedabove, the following embodiments further describe embodiments forpipeline implementations of the present delta-sigma ADC techniques. Inan exemplary embodiment, delta-sigma ADC is implemented using a pipelineFPGA architecture and corresponding operational principles with respectto timing and machine status. More particularly, conventionaldelta-sigma ADC techniques rely on sequential operation, which requiresnot only a high sampling rate, but also a high processing speed due tothe current output bit depending on both the current input and previousoutputs.

These conventional constraints are resolved by the present embodiments,which include an innovative pipeline technique for segmenting acontinuous input data stream, and then performing pipeline processingfor each segment thereof, thereby successfully trading the processingspeed for the hardware resourcing. According to these new systems andmethods, the speed requirement of FPGA may be significantly relaxed. Asdescribed further below, for a practical experimental implementationutilizing an input sampling rate of 5 GSa/s, a 32-pipeline architectureeffectively realized a reduction of the FPGA operation speed to 156.25MHz. Accordingly, the present inventors were able to successfullydemonstrate efficient implementation of delta-sigma digitization of 5Gand LTE signals without realizing a significant performance penalty frompipeline processing.

Referring back to FIG. 27, network architecture 2700 illustrates a C-RANin a 5G mobile network paradigm that advantageously simplifies eachBS-to-RRU connection, while also making hoteling, pooling, and cloudingof baseband processing possible, as well as enabling the coordinationamong multiple cells. Network portions 2702, 2704, 2706 represent threedistinct segments of the network, namely, the “backhaul,” the“fronthaul,” and the “midhaul,” respectively. Backhaul 2702 functions totransmit baseband bits from S-GW 2710 to CU 2712 using, for example, WDMcoherent optical links, midhaul 2706 connects CU 2712 with DUs 2716using digital fiber links based on IM/DD, and fronthaul 2704 deliversmobile signals from DU 2716 to RRUs 2714 in either analog or digitalwaveforms.

As described above, techniques have been proposed to increase spectralefficiency and reduce latency for fronthaul 2704, such as by, forexample, analog fronthaul based on RoF technology (e.g., analog linkportion 2808, FIG. 28) and digital fronthaul based on CPRI (e.g., firstdigital link portion 2810, FIG. 28). More particularly, in analog linkportion 2808, after baseband processing in DU 2802, mobile signals aresynthesized and up-converted to radio frequencies, and then transmittedas analog waveforms from DU 2802 to RRU 2804. For multiple bands ofmobile services, the mobile signals are aggregated in the frequencydomain before analog transmission. At RRU 2804, different mobile signalsmay be first separated by BPFs, and then amplified and fed to an antennafor wireless emission. In some embodiments high-RF layer devices (e.g.,local oscillator, mixer, etc.) may be consolidated in DU 2802, whereaslow-RF layer functions (e.g., filtering, amplification, etc.) may bedistributed in RRUs 2804. However, the analog fronthaul of analog linkportion 2808 will still experience nonlinear impairments due to thecontinuous envelope and high PAPR of mobile signals.

Conventional digital fronthaul techniques have attempted to avoid theseanalog impairments by employing a digital front haul interface based onCPRI (e.g., first digital link portion 2810). In the conventionalinterface, at DU 2802, a Nyquist ADC (e.g., ADC 2834) digitizes mobilesignals into bits, which are then transported to RRUs 2804 over digitalIM/DD fiber links. Since each signal is digitized in the baseband, its Iand Q components are digitized separately and multiplexed in the timedomain. At RRU 2804, after time division de-multiplexing (e.g., bydemultiplexer 2850), a Nyquist DAC (e.g., DAC 2848) is used to recoverthe analog waveforms of I/Q components, which are then up-converted toradio frequencies by RF local oscillator and mixer. CPRI-based digitalfronthaul techniques are therefore more resilient against nonlinearimpairments, as well as capable of employment within existing 2.5/10GPONs.

However, conventional CPRI-based digital fronthaul interfaces requireNyquist DAC and the all RF layer functions in each RRU, which increasesthe complexity and cost of cell sites. Additionally, as described above,CPRI is constrained by its low spectral efficiency, requiressignificantly high data rates after digitization, and only operates at afixed chip rate (e.g., 3.84 MHz) capable of accommodating only a fewRATs, such as UMTS (CPRI version 1 and 2), WiMAX (v3), LTE (v4), and GSM(v5). Moreover, because mobile signals are multiplexed using TDMtechnology, time synchronization is a particular problem for theCPRI-based digital fronthaul, which is not able to effectivelycoordinate the coexistence of these legacy RATs with the new andupcoming 5G services. The low spectral efficiency and lack ofcompatibility of CPRI renders it technically infeasible and costprohibitive to implement CPRI for the NGFI. Some conventional proposalssuggests these CPRI constraints may be circumvented using IQ compressionand nonlinear digitization, however, all CPRI-based solutions only dealwith baseband signals, which always require DAC and RF up-conversion atRRUs.

Referring back to FIG. 28, a new digitization interface (e.g., seconddigital link portion 2812) avoids the constraints at the CPRI-basedsolution by implementing a delta-sigma ADC that is capable of tradingquantization bits for sampling rate. That is, the delta-sigma ADC uses asampling rate much higher than the Nyquist rate, but only onequantization bit. Therefore, unlike the Nyquist ADC in CPRI, which onlyhandles baseband signals, the delta-sigma ADCs may effectively functionin lowpass or bandpass, and digitize mobile signals at baseband, or “ASIS” at RFs without frequency conversion. Using bandpass delta-sigma ADC(e.g., ADC 2858), mobile signals may be up-converted to RFs andmultiplexed in the frequency domain at DU 2802, and then digitized “ASIS” before delivery to RRU 2804, where, instead of a conventional DAC, asimple, low-cost filter (e.g., BPF 2864) may be used to retrieve theanalog waveform, which is thus already at RF for wireless transmission.

Accordingly, when compared with CPRI, the innovative delta-sigmadigitization techniques of the present embodiments both improves thespectral efficiency, and also simplifies the RRU by consolidatinghigh-RF layer functions in the DU, thereby advantageously leaving onlylow-RF layer functions in the RRU. Through these advantageous systemsand methods, a new NGFI functional split option (e.g., split option 9,FIG. 27) may be achieved between the high-RF and low-RF layers.Referring back to FIG. 27, CPRI is capable of adopting only split option8, by leaving DAC and all RF layer functions in each RRU. According tothe present embodiments, hand, the RoF-based analog fronthaul (e.g.,analog link portion 2808) and delta-sigma ADC-based digital fronthaul(e.g., second digital link portion 2812) may adopt split option 9 tosimplify the RRU by centralizing RF up-conversion in the DU andreplacing conventional DAC in the RRU by a low-cost BPF. Thisadvantageous architecture thereby enables a DAC-free cell site, havingsimplified RF, which facilitates new 5G small cell deployment.Additionally, through frequency division multiplexing techniques, thedelta-sigma digitization interface is able to heterogeneously aggregatemultiband mobile services from different RATs, which thereby circumventsthe clock rate compatibility issues and time synchronization problems ofCPRI, as described above with respect to FIG. 5, and the correspondingNyquist ADC operational principles. Nevertheless, the presentembodiments are capable of integration and coexistence with these legacytechniques as such are presently employed, or as may be modified asdescribed further below with respect to FIG. 35.

FIG. 35 is a schematic illustration of a parallel quantization ADCarchitecture 3500. In an exemplary embodiment, architecture 3500 isimplemented as a modified Nyquist ADC. That is, in exemplary operation,architecture 3500 receives an analog signal 3502 at a sampling unit3504, which samples (at the Nyquist sampling rate, in this example)analog signal 3502 into an input sample stream 3506. Input sample stream3506 is received by de-serialization unit 3508, which is configured tode-serialize input sample stream 3506 into first and second paralleldata streams 3510(1) and 3510(2), respectively. First and secondparallel data streams 3510(1), 3510(2) are then both quantized inparallel by their own respective multi-bit quantization unit 3512, andthe results therefrom are serialized, by a serialization unit 3514, intoa single output bit stream 3516. In an exemplary embodiment, thede-serialization/serialization operations of units 3508/3514 areconfigured to implement time interleaving.

For example, in the case where input sample stream 3506 is a 5 GSa/ssample stream, de-serialization unit 3508 may separate the 5 GSa/sstream into two parallel 2.5 GSa/s data streams (e.g., first and secondparallel data streams 3510 (1), 3510 (2)), which may place even samplesin one stream and odd samples in the other. Serialization unit 3514 maythen, after quantization by quantization units 3512, interleave the twoparallel quantized streams in the time domain. Architecture 3500 istherefore similar, in some respects, to the architecture described abovewith respect to digitization process 500, FIG. 5, except thatarchitecture 3500 additionally de-serializes the Nyquist sampled signalprior to quantization, and then quantizes each de-serialized stream inparallel. This modified de-serialization, parallel quantization,serialization approach may be effectively implemented with a CPRI-baseddigital interface using a Nyquist ADC because the Nyquist ADC digitizeseach sample individually, and thus the quantization bits are determinedonly by amplitude, with no dependence on previous samples.

FIG. 36 is a graphical illustration depicting an operating principle ofa delta sigma digitization process 3600. As described above, the presentdelta-sigma ADC techniques may be implemented for either lowpass orbandpass configurations. Accordingly, digitization process 3600 issimilar to digitization process 600, FIG. 6, except that digitizationprocess 600 illustrates an example of lowpass ADC, whereas digitizationprocess 3600 illustrates an example of bandpass ADC.

In exemplary operation of process 3600, the analog input signal (notshown in FIG. 36) is digitized, by an oversampling subprocess 3602, “ASIS” without frequency conversion, into a sampled data stream 3604. In anembodiment, oversampling subprocess 3602 uses a high sampling rate toextend the Nyquist zone about a signal band 3606, and spreadquantization noise 3608 over a wide frequency range. A noise shapingsubprocess 3610 then pushes quantization noise 3608′ out of signal band3606, thereby separating the signal from noise in the frequency domain.In an exemplary embodiment, quantization noise 3608 from a quantizationunit 3612 (1-bit, in this example) is OoB, and process 3600 implementsbandpass delta-sigma ADC to transform the signal waveform from analog todigital by adding OoB quantization noise 3608, but leaving the originalspectrum of signal band 3606 intact.

Accordingly, in a filtering subprocess 3614, a BPF 3616 is configured toretrieve an output analog waveform 3618 by filtering out OoBquantization noise 3608′ without any need for conventional DAC. Thus,BPF 3616 effectively provides the functionality of both of theconventional DAC and frequency de-multiplexers for multiband mobilesignals. In some embodiments, retrieved analog signal 3618 may have anuneven noise floor from the noise shaping technique of noise shapingsubprocess 3610.

FIG. 37 is a schematic illustration of a delta-sigma ADC feedbackarchitecture 3700. Architecture 3700 is similar to architecture 3500,FIG. 35, except that, whereas architecture 3500 operates in parallel,architecture 3700 operates sequentially. That is, although conventionalNyquist ADC may be modified into the parallel serialization ofarchitecture 3500, delta-sigma ADC operates in a sequential manner.

In exemplary operation, architecture 3700 receives an analog signal 3702at an oversampling unit 3704, which oversamples (at OSR*f_(s)/2, in thisexample) analog signal 3702 into an input sample stream 3706. Inputsample stream 3706 is received by a noise shaping filter 3708, and thena quantizer 3710 (1-bit, in this example), to produce an output bitstream 3712. In an exemplary embodiment, architecture 3700 furtherincludes a feedback loop 3714 from output bit stream 3712 to noiseshaping filter 3708. Feedback loop thus enables architecture 3700 toconfigure the output bits of output bit stream 3712 to not only dependon a current input sample, but also on one or more previous outputs.

Because this dependence on consecutive output bits rendersde-serialization and parallel processing of the input sample streamdifficult, in the exemplary embodiment, architecture 3700 is configuredto implement delta-sigma ADC at a high sampling rate and high processingspeed enable the associated FPGA (not shown in FIG. 37) to adequatelyfollow the feed-in speed of input samples. However, typical conventionalFPGAs are known to operate at only hundreds of MHz, whereas GHz samplingrates and greater are required for delta-sigma digitization of LTE/5Gsignals. Therefore, a functional gap exists between the minimum samplingrates of the present delta-sigma ADC techniques and the considerablyslower operational speeds of conventional FPGAs. This gap is bridgedaccording to the innovative pipeline systems and methods describedbelow.

FIG. 38 is a schematic illustration of a pipeline architecture 3800 fora delta sigma digitization process. Pipeline architecture 3800advantageously functions to relax the FPGA speed requirements describedimmediately above, but without introducing a significant performancepenalty thereby. Accordingly, pipeline architecture 3800 represents astructural alternative to FPGA 2904 of system architecture 2900, FIG.29.

In an exemplary embodiment, pipeline architecture 3800 includes ananalog input source 3802 at an ADC interface 3804 configured to realizea 5 GSa/s 10-bit delta-sigma ADC. In exemplary operation, ADC interface3804 samples the signal of input analog source 3802 at 5 GSa/s, with 10bits per sample. A segmentation unit 3806 segments the continuous streamof input samples into 32 blocks (i.e., pipelines) at an input buffer3808, and sequentially fed to 32 respective input first-in-first-outbuffers (FIFOs) 3810 for each pipeline. In an exemplary embodiment, eachinput FIFO 3810 is a 10-bit buffer, that is, ADC interface 3804 provides10 quantization bits for each sample in this example. Accordingly, inthis embodiment, each input FIFO 3810 stores W samples, and thus has asize of at least 10 W bits.

In each pipeline, once the respective input FIFO 3810 is filled, therespective data therefrom is fed to a delta-sigma modulator 3812, whichperforms delta-sigma digitization to transform the respective 10 inputbits (in this example) to a single output bit. The delta-sigmadigitization by delta-sigma modulator 3812 is performed in parallel withother pipelines. In an embodiment, delta-sigma modulator 3812 mayconstitute 32 respective individual delta-sigma modulation units. Therespective output bits from the pipelines of modulator 3812 may then bestored in a respective output FIFO 3814 of an output buffer 3816. Thesize of each output FIFO 3814 is therefore only 1/10 the size of eachinput FIFO 3810. In a practical implementation of pipeline architecture3800 for a conventional FPGA, ΔW more samples may be allocated both toinput FIFOs 3810 and output FIFOs 3814, such that the respective sizesthereof become 10*(W+ΔW) bits and W+ΔW bits.

In other words, the output bits after digitization are stored in 32separate output FIFOs 3814(1-32), and then combined, by a cascading unit3818, into a single stream of output bits at an MGT port 2914 (5 Gb/sMGT-SMA, in this example). Enabled with the pipeline design of pipelinearchitecture 3800, the operation speed of each pipeline isadvantageously reduced to 1/32*5 GSa/s=156.25 MSa/s. Accordingly, theFPGA clock rate may be effectively relaxed to 156.25 MHz withoutsignificant performance penalty.

It may be noted that the segmentation operation of segmentation unit3806 in pipeline architecture 3800 differs from the de-serializationoperation of de-serialization unit 3508, of parallel quantization ADCarchitecture 3500, FIG. 35, in that the samples of each segmented blockin pipeline architecture 3800 is consecutive. That is, in the firstblock, the segmented samples will be sample 0 through sample W-1 in thefirst block. The second block will thus contain samples W through 2W-1,and so on. In contrast, the samples of parallel quantization ADCarchitecture 3500 are time interleaved, such that first parallel datastream 3510 (1) contains even samples 0, 2, 4, 6, etc., and secondparallel data stream 3510 (2) contains samples 1, 3, 5, 7, etc. Atoutput bit stream 3516 of parallel quantization ADC architecture 3500,the samples from different streams are time interleaved; whereaspipeline architecture 3800, the output blocks from respective outputFIFOs 3814s may be simply cascaded (e.g., by cascading unit 3818, oneafter another.

Because delta-sigma ADC relies on sequential operation, there may besome performance penalty encountered when segmenting a continuous samplestream into a plurality of blocks. In theory, the smaller is the blocksize, the larger will be the penalty introduced. Accordingly, in areal-time practical implementation of the exemplary embodimentsdescribed immediately above, a 5-GSa/s 32-pipeline ADC was used,together with a selected block size of W=20K, which establishes thetradeoff between the performance penalty and use of memory on the FPGA.In a further embodiment, a ΔW=2K margin value is added to each FIFO3810, 3814 for greater ease of implementation and relaxation of the timeconstraint.

FIG. 39 is a flow diagram of an input state process 3900 for pipelinearchitecture 3800, FIG. 38. In an exemplary embodiment, process 3900illustrates an operational principle of pipeline architecture 3800 usinga state machine flow diagram for input FIFO (e.g., input FIFOs 3810,FIG. 38) operation. In exemplary operation, process 3900 begins at step3902, in which the FPGA is powered on, and all input FIFOs enter an IDLEstate. In an exemplary embodiment, all input FIFOs stay in the IDLEstate until enablement of a start signal. Step 3904 is a decision step.In step 3904, process 3900 determines whether a start signal has beenreceived. If no start signal has been received, process 3900 returns tostep 3902, and all input FIFOs remain in the IDLE state. If, in step3904, process 3900 determines that a start signal has been received,process 3900 proceeds to step 3906, in which the input FIFOs enter aHOLD state. In an exemplary embodiment, the input FIFOs remain in theHOLD state, for a certain amount of clock cycles and/or until all inputFIFOs have completely come out of the IDLE state.

In step 3908, all input FIFOs enter a WRITE ONLY state. In an embodimentof step 3908, the input FIFOs enter the WRITE ONLY state simultaneously,or upon each respective input FIFO coming out of the HOLD state, if thetiming is not simultaneous between the FIFOs. In other embodiments, step3908 may be performed sequentially for each input FIFO. In step 3910,the first input FIFO in the pipeline sequence (FIFO 0, in this example)is filled with input signal sample data corresponding to that FIFOblock. In an exemplary embodiment of step 3910, once the input FIFO isfilled, the input FIFO transits from the WRITE ONLY state to aREAD&WRITE state. Step 3910 is then repeated sequentially for each ofthe N input FIFOs until the last of N input FIFOs in the sequence (FIFON−1, in this example) is filled and transitions from the WRITE ONLYstate to READ&WRITE state. Once an input FIFO is filled and set to theREAD&WRITE state, the respective input FIFO will stay in the READ&WRITEstate permanently, until receiving a RESET signal in step 3912.

Accordingly, assuming that the input ADC has a sampling rate of f_(s),then the FPGA throughput will be f_(s) samples per second. Furthermore,given that there are N pipelines, the operation speed of each individualpipeline is now advantageously reduced to f_(s)/N, which is also theclock rate of FPGA. Thus, within each clock cycle, N samples arereceived from the input ADC, and then fed into the N input FIFOssequentially. In this particular example, it is assumed that the size ofeach input FIFO is W samples. Accordingly, it will take W/N clock cyclesto fill one FIFO with the relevant input sample data, and a total of Wclock cycles to fill all N the input FIFOs.

FIG. 40 is a timing diagram 4000 for operation of input buffer 3808,FIG. 38. For illustrated purposes, and not in a limiting sense, timingdiagram 4000 is depicted using the assumptions that the number ofpipelines N=4, and that the input FIFO size W=8. The person of ordinaryskill in the art though, will understand that the number of pipelinesand the input FIFO size may differ according to the particular needs ofthe system design.

In an exemplary embodiment, timing diagram 4000 is implemented withrespect to a clock signal 4002, a reset signal 4004, a state sequence4006, and an input data (Data In) sequence 4008. In exemplary operationof timing diagram 4000, each of N (four, in this example) input FIFOs4010 are written sequentially, so that a respective write enable signal4012 are turned on periodically, according to a duty cycle of 1/N. Onceinput FIFOs 4010 enter the READ&WRITE state of state sequence 4006, itmay be seen that all respective read enable signals 4014 are always on.In this example, different from the sequential writing process, all Ninput FIFOs 4010 may be read out (e.g., Data Out sequences 4016)simultaneously. Nevertheless, in this example, one sample is read outfrom each input FIFO 4010 for each cycle of clock signal 4002.Accordingly, for the embodiment depicted in FIG. 40, it will take Wclock cycles to deplete a FIFO 4010.

FIG. 41 diagram is a flow diagram of an output state process 4100 forpipeline architecture 3800, FIG. 38. In an exemplary embodiment, process4100 illustrates an operational principle of pipeline architecture 3800using a state machine flow diagram for output FIFO (e.g., output FIFOs3814, FIG. 38) operation. Process 4100, for the output FIFO operation,is somewhat similar to process 3900, FIG. 39 for the input FIFOoperation but differs in some respects.

For example, in exemplary operation, process 4100 begins at step 4102,in which the output FIFOs enter the IDLE state once power is on. Step4104 is a decision step, in which process 4100 determines whether astart signal has been received. If no start signal has been received,process 4100 returns to step 4102, and the output FIFOs remain in theIDLE state. However, if process 4100 determines that a start signal hasbeen received, process 4100 proceeds to step 4106, in which the outputFIFOs enter the HOLD state, and remain in this state for a certainamount of clock cycles until all output FIFOs have come out of the IDLEstate.

In step 4108, all output FIFOs enter the WRITE ONLY state. Step 4108thus differs from step 3908 of process 3900, FIG. 39, in that all of theoutput FIFOs are filled simultaneously in step 4108, whereas in step3908, the input FIFOs are filled sequentially. In step 4110, all outputFIFOs will transit to the READ&WRITE state at the same time, and stay inthis state permanently until, in step 4112, receiving a RESET signal.

FIG. 42 is a timing diagram 4200 for operation of output buffer 3816,FIG. 38. In the exemplary embodiment depicted in FIG. 42, forillustrative purposes, the same number of pipelines N =4, and the sameFIFO size W=8, are used as in the example depicted in FIG. 40, above.The person of ordinary skill in the art though, will understand that thenumber of pipelines and the output FIFO size may also differ accordingto the particular needs of the system design.

In the exemplary embodiment, timing diagram 4200 is implemented withrespect to a clock signal 4202, a reset signal 4204, a state sequence4206, and an output data (Data Out) sequence 4008. In exemplaryoperation of timing diagram 4200, each of four output FIFOs 4210 arewritten simultaneously, with only one sample written (e.g., Data Insequences 4212) per cycle of clock signal 4202. That is, because alloutput FIFOs are written simultaneously, respective write enable signals4214 are always on. This operation is different from operation of inputFIFOs 4010, FIG. 40, which are written sequentially with N samplescoming per clock cycle. On the other hand, output FIFOs 4210 are readout sequentially, and thus respective read enable signals 4216 areturned on periodically with a duty cycle of 1/N. Accordingly, all outputFIFOs 4210 are filled at the same time, but W cycles are needed tocompletely write all samples (i.e., one sample written per clock cycle).In contrast, input FIFOs 4010 require W/N clock cycles to fill one inputFIFO. Therefore, within each clock cycle, N samples are read out fromeach output FIFO 4210, thereby requiring W/N clock cycles to deplete anoutput FIFO, and W cycles total to read from all N output FIFOs.

The embodiments described above were demonstrably implemented using a4th-order bandpass delta-sigma ADC for an FPGA employing filter 1600,FIG. 16A. More particularly, the cascaded resonator feedforward (CRFF)structure of filter 1600 provides four cascaded stages of feedback loops1606 (z⁻¹), with the outputs of the four stages being fed forward to acombiner before quantizer 1610 (1-bit, in this example), withcoefficients of a₁, a₂, a₃, and a₄. Each pair of two stages may then becascaded together to form a resonator including a different feedbackpath in each resonator (e.g., g₁ and g₂). Quantizer 1610 thus serves tofunction (in 1-bit mode) as a comparator, which then outputs a one-bitOOK signal. By adjusting the coefficients of a and g, variousdelta-sigma ADCs with different OSRs and passbands may be implemented.

FIG. 43 is a flow diagram for a fixed point coefficient implementationprocess 4300. In an exemplary embodiment, process 4300 is implementedwith respect to the FPGA implementation procedure illustrated in Table6, below. This example, an initial delta-sigma ADC design of is based ona floating-point simulation in MATLAB without pipeline processing, witha final goal being a real-world fixed-point FPGA implementation withpipeline processing. Process 4300 thus serves to bridge the gap betweensimulation and implementation, by effectively evaluating the performancedegradation during the transition from floating-point to fixed-point,and also the penalty induced by pipeline processing. In the exemplaryembodiment, process 4300 is configured to isolate at least one reasonfor performance degradation in each of the several subprocesses shown inTable 6 and generate a systematic implementation procedure therefrom.

TABLE 6 Performance Sub- penalty Process Implementation CoefficientsCalculation Pipeline Input Output from last step 1 MATLAB FloatingFloating No Ideal Logic Best performance simulation signal levels 2Verilog Floating Floating No Ideal Logic Identical with simulationsignal levels Subprocess 1 3 Verilog Fixed Floating No Ideal LogicDifferent simulation signal levels coefficients 4 Verilog Fixed Fixed NoIdeal Logic Fixed-point simulation signal levels intermediate variables5 Verilog Fixed Fixed Yes Ideal Logic Input data stream simulationsignal levels segmentation 6 FPGA Fixed Fixed Yes Ideal Logic Timeconstraint signal levels

In exemplary operation, process 4300 begins at step 4302, in whichSubprocess 1 of Table 6 is executed. In an exemplary embodiment ofSubprocess 1, a delta-sigma ADC is designed based on floating-pointMATLAB simulation without pipeline processing, and the ADC performancethereof is optimized since the penalty due to fixed-point approximation,hardware constraint, and pipeline processing have not been yet included.

Process 4300 then proceeds to step 4304, in which Subprocess 2 of Table6 is executed. In an exemplary embodiment of Subprocess 2, thefloating-point design from Subprocess 1 is translated from MATLAB tohardware description language, such as Verilog. Step 4306 is a decisionstep. In step 4306, process 4300 determines whether the respectiveperformances of Subprocesses 1 and 2 are substantially identical, whichwould be expected. If the performances are not substantial identical,process 4300 proceeds to step 4308, in which the Verilog code isdebugged, and then step 4304 is repeated. If, however, in step 4306, therespective performances of Subprocesses 1 and 2 are found to besubstantially identical, process 4300 proceeds to step 4310.

In step 4310, Subprocess 3 of Table 6 is executed. In an exemplaryembodiment of Subprocess 3, key coefficients (e.g., a and g) of thedelta-sigma ADC are approximated from floating-point to fixed-point,while keeping all intermediate calculations still in the floating-pointmode. In comparison with Subprocess 2, some performance degradation inSubprocess 3 will be expected, due to the difference betweenfloating-point and fixed-point coefficients. Step 4312 is therefore adecision step. In step 4312, process 4300 determines whether theperformance degradation greater than an expected, tolerable value, andtherefore not acceptable. If process determines, at step 4312, but theperformance degradation is not acceptable, process them proceeds to step4314, in which key parameters may be identified to provide a betterapproximation Subprocess 3. Accordingly, process 4300 proceeds from step4314, to step 4316, in which a better approximation is achieved, forexample, by adjusting the bit number of each coefficient. In anembodiment of Subprocess 3, steps 4310 through 4316 may be repeated anumber of times, over a few trials, which may be needed to identify thecoefficients that have most impact on the final performance. Throughthis embodiment of Subprocess 3, the bit numbers may be fine-tuned untilsatisfactory performance is achieved.

After successful values are achieved through Subprocess 3, process 4300proceeds to step 4318. In step 4318, process 4300 executes Subprocess 4.In an exemplary embodiment of Subprocess 4, all the intermediatecalculations and variables are transformed from floating-point tofixed-point. Due to the limited bit number, further performancedegradation may be expected. Accordingly, process 4300 proceeds to step4320. Step 4320 is a decision step. If, in step 4320, process 4300determines that there is performance degradation is greater thanacceptable limit (e.g., predetermined), then the degradation is notacceptable, in process 4300 proceeds to step 4322, in which process 4300identifies key parameters having the most impact, and then to step 4324,in which process 4300 adjusts the bit number of each intermediatevaluable to identify those most impactful parameters, and thenfine-tunes their bit numbers to achieve satisfactory performance. Aswith Subprocess 3, it might require several iterations to find the keyparameters and adjust their bit numbers. Thus, it can be seen that, ineach of Subprocesses 1-4, no segmentation was considered. That is, theinput data stream is processed continuously without interruption.

Accordingly, on the completion of Subprocess 4, process 4300 proceeds tostep 4326, in which Subprocess 5 is executed. In an exemplary embodimentof Subprocess 5, pipeline processing is added, similar to the techniquesdescribed above, which segments the continuous input data stream intoseveral blocks, and then performs fixed-point calculation on each suchsegmented block. Because delta-sigma digitization is a sequentialprocessing technique (e.g., the current output bit may depend on bothcurrent and previous input samples), segmentation of continuous inputdata stream will be expected to degrade the performance, and thisdegradation penalty will increase as the block size decreases. Indecision step 4328, process 4300 determines if this degradation penaltyis less than the predetermined level acceptable performance degradation.If process 4300 determines that the performance degradation is notacceptable, process 4300 proceeds to step 4330, in which a block size isadjusted. Process 4300 then returns to step 4326. If process 4300determines that the degradation is less than an acceptable level,process 4300 proceeds to step 4332.

In step 4332, Subprocess 6 of Table 6 is executed. In an exemplaryembodiment of step 4332, Subprocess 6 is executed as a real-world FPGAimplementation. In contrast, and as indicated in Table 6, Subprocesses 2through 5 were performed as Verilog simulations. Accordingly, in anoptional step 4334, prior to evaluating the performance of the FPGA,process 4300 may first determine whether the FPGA meets the timeconstraint. For example, given an input ADC operating at 5 GSa/s,segmented into 32 pipelines, the operation speed of each pipeline shouldbe 156.25 MHz. That is, to satisfy the time constraint, the delta-sigmamodulation in each pipeline should be completed within 1/156.25 MHz =6.4ns. If, in step 4334, the FPGA cannot meet the time constraint, process4300 may proceed to step 4336, in which process 4300 may optionallyperform a tradeoff evaluation between the performance penalty and thememory consumption, and then calculate an appropriate block size for theoptimum balance between performance and memory. In an exemplaryembodiment of step 4336, the bit numbers of key coefficients andintermediate valuables are fine-tuned and fed to one or more ofSubprocess 3 (e.g., at step 4316) and Subprocess 4 (e.g., at step 4324).

Referring back to FIG. 38, where input FIFOs 3810 and output FIFOs 3814are used in each pipeline to buffer the segmented data stream, too largeof a block size may result in an undesirable increase in memory usage onthe FPGA. In general, reducing the number of bits will improve the FPGAspeed, while at the same time, degrade the system performance.Accordingly, the tradeoff operations of step 4336 may be particularlyeffective to ensure that the FPGA meets the time constraints withoutsacrificing too much performance. In the real-world implementationembodiment described herein, a block size of W=20K samples, and having a10% margin (i.e., ΔW=2K), was used.

Process 4300 then proceeds to step 4338. Step 4338 is a decision step,in which process 4300 evaluates the performance of the FPGAimplementation, and determines whether the performance demonstrates anacceptable degradation value. If the performance degradation of the FPGAis not within acceptable limits, process 4300 proceeds to step 4340, inwhich a debugging operation of the FPGA is performed, and step 4332 isthen repeated. If, however, in step 4338, process 4302 determines thatthe performance degradation is acceptable, process 4300 proceeds to step4342, and completes the implementation.

FIG. 44 is a schematic illustration of an exemplary testbed 4400. In anexemplary embodiment, testbed 4400 represents a real-worldimplementation used to prove the concept of, and verify, the pipelinedesign of pipeline architecture 3800, FIG. 38, using the implementationprocedure of fixed point coefficient implementation process 4300, FIG.43. As implemented, testbed 4400 was similar in structure andfunctionality to fronthaul system 2906, FIG. 29.

In the exemplary embodiment, testbed 4400 includes a transmitting DU4402 in operable communication with an RRU 4404 over a transport medium4406 (30 km SMF, and this example). In this embodiment, DU 4402 includesa transmitting AWG 4408 (e.g., Tektronix 7122 C AWG) for generatingreal-time LTE and 5G signals, an attenuator 4410, and an FPGA 4412(e.g., Xilinx Virtex-7 FPGA on a VC707 development board) forimplementing real-time bandpass delta-sigma ADC. FPGA 4412 includes aninput ADC interface 4414 (e.g., a 4DSP FMC170) and an output port 4416(e.g., a multi-gigabit transceiver (MGT)-SubMiniature version A (SMA)connector). In this example, FPGA 4412 implemented a 5 GSa/s 1-bitbandpass delta-sigma ADC for the digitization of LTE and 5G analogsignals 4418 from transmitting AWG 4408, that is, a sampling rate of 5GSa/s and 10 quantization bits per sample. In exemplary operation of DU4402, analog signals 4418 are input to ADC interface 4414, where theyare first digitized to 10 bits, and then transmitted to FPGA 4412, wherethe delta-sigma digitization transforms the 10 input bits to one outputbit. FPGA 4412 then outputs a 5-Gb/s OOK signal 4420 at output port4416. In this implementation, to relax the speed of FPGA 4412, a32-pipeline architecture (e.g., FIG. 38) is used to reduce the FPGAclock rate to 156.25 MHz.

In further exemplary operation of testbed 4400, digitized 5-Gb/s OOKsignal 4420 was then used to drive an optical modulator 4422 (e.g., a12.5 Gb/s Cyoptics DFB+EAM) for transmitting signal 4420 as a modulatedoptical signal over a transport medium 4406 to RRU 4404. At RRU 4404,the optical signal is received by a photodetector 4424, captured by aDSO 4426 (e.g., a 20 GSa/s Keysight DSO), and then processed by a DSP4428 (e.g., a MATLAB DSP) for bandpass filtering by a BPF 4430 toretrieve the analog waveform at an LTE/5G receiver 4432.

In further real-world operation of testbed 4400, LTE/5G signals 4414were generated according to the OFDM parameters listed in Table 5,above. Similarly, EVM performance requirements were according to thevalues listed in Table 3, above, except for the case of the 1024QAMmodulation format, which is not yet specified. In implementation oftestbed 4400 instead of the 1% value listed in Table 3, 2% was used forthe 1024QAM EVM performance requirement as a temporary criterion. Usingthese values, testbed 4400 was tested under various operations accordingto the exemplary implementation scenarios listed in Table 4, above, andproduced experimental results therefrom substantially consistent withthe results described with respect to FIGS. 30A-34B.

Therefore, according to the innovative systems and methods presentedherein, a real-time FPGA-based bandpass delta-sigma ADC is provided fordigitizing LTE and 5G signals having significantly higher reportedsampling rate and wider signal bandwidth then any previously-reportedsignals from conventional systems and known implementations. The presentbandpass delta-sigma ADC techniques are capable of digitizing the 5G/LTEsignals “AS IS” at RFs without the need of frequency conversion, therebyfurther enabling the new function split option between the high-RF andlow-RF layers. Thus, the present delta-sigma ADC-based digital fronthaulinterface still further reduces the RRU cost and complexity, while alsofurther facilitating even wider deployment of 5G small cells.

The present systems and methods still further provide innovativepipeline architectures and processes for delta-sigma ADC thatsignificantly relax the FPGA speed requirement, thereby enabling theimplementation of high-speed delta-sigma ADC, even using relativelyslow-speed FPGA, but without significantly sacrificing performance. Thepresent embodiments still further introduce innovative evaluationprocess that is configured to transform a floating-point simulation to afixed-point FPGA implementation, to further optimize the design of thepipeline systems and methods described herein.

Exemplary embodiments of delta-sigma digitization systems, methods, andreal-time implementations are described above in detail. The systems andmethods of this disclosure though, are not limited to only the specificembodiments described herein, but rather, the components and/or steps oftheir implementation may be utilized independently and separately fromother components and/or steps described herein. Additionally, theexemplary embodiments described herein may be implemented and utilizedin connection with access networks other than MFH and MBH networks.

Although specific features of various embodiments of the disclosure maybe shown in some drawings and not in others, this is for convenienceonly. In accordance with the principles of the disclosure, a particularfeature shown in a drawing may be referenced and/or claimed incombination with features of the other drawings.

Some embodiments involve the use of one or more electronic or computingdevices. Such devices typically include a processor or controller, suchas a general purpose central processing unit (CPU), a graphicsprocessing unit (GPU), a microcontroller, a reduced instruction setcomputer (RISC) processor, an application specific integrated circuit(ASIC), a programmable logic circuit (PLC), a field programmable gatearray (FPGA), a DSP device, and/or any other circuit or processorcapable of executing the functions described herein. The processesdescribed herein may be encoded as executable instructions embodied in acomputer readable medium, including, without limitation, a storagedevice and/or a memory device. Such instructions, when executed by aprocessor, cause the processor to perform at least a portion of themethods described herein. The above examples are exemplary only, andthus are not intended to limit in any way the definition and/or meaningof the term “processor.”

This written description uses examples to disclose the embodiments,including the best mode, and also to enable any person skilled in theart to practice the embodiments, including making and using any devicesor systems and performing any incorporated methods. The patentable scopeof the disclosure is defined by the claims, and may include otherexamples that occur to those skilled in the art. Such other examples areintended to be within the scope of the claims if they have structuralelements that do not differ from the literal language of the claims, orif they include equivalent structural elements with insubstantialdifferences from the literal language of the claims.

What is claimed is:
 1. A delta-sigma digitization interface fordigitizing an input analog signal into a digitized bit stream,comprising: a sampling unit configured to sample the input analog signalat a predetermined sampling rate to produce a sampled analog signal; asegmentation unit configured to segment the sampled analog signal into aplurality of separate data pipelines; a delta-sigma analog-to-digitalconverter (ADC) configured to individually quantize a respective signalsegment contained within each of the plurality of data pipelines into adigitized bit stream segment according to a predetermined number ofquantization bits; a cascading unit configured to combine the respectivequantized signal segments into a single digitized output stream; and anoutput port for transmitting the single digitized output stream to atransport medium as the digitized bit stream, wherein the predeterminednumber of quantization bits is one, and wherein the delta-sigma ADC isfurther configured to perform one-bit quantization according to a binaryquantization protocol.
 2. The interface of claim 1, wherein thedelta-sigma ADC comprises a field programmable gate array (FPGA).
 3. Theinterface of claim 1, wherein the binary quantization protocol compriseson-off keying.
 4. The interface of claim 2, wherein the sampling unitcomprises an FPGA mezzanine card (FMC).
 5. The interface of claim 4,wherein the FMC is configured to sample the input analog signal at 5GSa/s.
 6. A delta-sigma digitization interface for digitizing an inputanalog signal into a digitized bit stream, comprising: a sampling unitconfigured to sample the input analog signal at a predetermined samplingrate to produce a sampled analog signal; a segmentation unit configuredto segment the sampled analog signal into a plurality of separate datapipelines; a delta-sigma analog-to-digital converter (ADC) configured toindividually quantize a respective signal segment contained within eachof the plurality of data pipelines into a digitized bit stream segmentaccording to a predetermined number of quantization bits; a cascadingunit configured to combine the respective quantized signal segments intoa single digitized output stream; an output port for transmitting thesingle digitized output stream to a transport medium as the digitizedbit stream; and an input buffer logically disposed between thesegmentation unit and the delta-sigma ADC, wherein the input buffer isconfigured to distribute contiguous data portions of the sampled analogsignal in sequential order to respective ones of the plurality of datapipelines.
 7. The interface of claim 6, wherein the input buffercomprises an input first-in-first-out unit (FIFO) for each of theplurality of data pipelines.
 8. The interface of claim 7, wherein theinput FIFO utilizes at least 10-bits to store each sample of the sampledanalog signal in the input buffer.
 9. The interface of claim 8, whereinthe input FIFO is configured to store W samples, where W represents apositive integer, wherein the input FIFO has a size of at least 10 Wbits, and wherein each of the W samples is stored in a 10-bit buffer.10. The interface of claim 6, further comprising an output bufferincluding a separate output first-in-first-out unit (FIFO) for each ofthe plurality of data pipelines.
 11. The interface of claim 10, whereinthe output FIFO utilizes at least 1-bit to store segments of the sampledanalog signal in the output buffer.
 12. The interface of claim 11,further comprising an input FIFO, wherein a size of the output FIFO isone tenth of the size of the input FIFO.
 13. The interface of claim 2,wherein a clock rate of the FPGA for each of the plurality of datapipelines is approximately 156.25 MHz or less.
 14. The interface ofclaim 2, wherein the FPGA is disposed within a distributed unit (DU) ofa communication network.
 15. The interface of claim 1, wherein the inputanalog signal comprises at least one of a 5G new radio (5GNR) signal anda long term evolution (LTE) signal.
 16. A method of optimizing adelta-sigma analog-to-digital converter (ADC) architecture for a fieldprogrammable gate array (FPGA), comprising the steps of: simulating aperformance of the delta-sigma ADC according to a first floating-pointcalculation using floating-point coefficients of the delta-sigma ADC;approximating key coefficients from a floating-point format to afixed-point format; performing a second floating-point calculation ofthe delta-sigma ADC performance using the approximated key fixed-pointcoefficients; performing a first fixed-point calculation of thedelta-sigma ADC performance for a continuous input data stream usingfixed-point coefficients obtained from performance of the secondfloating-point calculation; performing a second fixed-point calculationof the delta-sigma ADC performance, wherein the continuous input datastream is segmented into a plurality of separate data blocks, andwherein the second fixed-point calculation is individually performed oneach separate segmented data block; and evaluating performance of theFPGA having a logical structure based on the performance of the secondfixed-point calculation individually performed on each of the pluralityof separate data blocks.
 17. The method of claim 16, wherein the stepsof performing the first and second fixed-point calculations areindividually repeated until a calculated degradation of the delta-sigmaADC performance is within a predetermined acceptable value.
 18. Themethod of claim 16, wherein the logical structure comprises a pipelinearchitecture, and further comprising, after the step of evaluating, astep of determining whether the operational speed of each pipeline inthe pipeline architecture meets an operational speed time constraint ofthe FPGA.
 19. The interface of claim 1, wherein the delta-sigma ADCcomprises a processor and a memory, and wherein the memory containscomputer-executable instructions that, when executed by the processor,cause the delta-sigma ADC to implement a delta-sigma algorithm.
 20. Theinterface of claim 1, wherein the delta-sigma ADC comprises anapplication specific integrated circuit (ASIC) configured to implement adelta- sigma algorithm.